Connected food preparation system and method of use

ABSTRACT

A connected oven, including a set of in-cavity sensors and a processor configured to automatically identify foodstuff within the cooking cavity, based on the sensor measurements; and automatically operate the heating element based on the foodstuff identity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/008,478 filed 14 Jun. 2018, which is a continuation of U.S.application Ser. No. 15/450,546, filed 6 Mar. 2017, now issued as U.S.Pat. No. 10,024,544, which is a continuation of U.S. application Ser.No. 15/147,597, filed 5 May 2016, now issued as U.S. Pat. No. 9,644,847,which claims the benefit of U.S. Provisional Application No. 62/157,325filed 5 May 2015, each of which are incorporated in their entirety bythis reference.

TECHNICAL FIELD

This invention relates generally to the food preparation field, and morespecifically to a new and useful oven in the food preparation field.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is an isometric view from the top right of a variation of theoven.

FIG. 2 is an isometric view from the bottom left of a variation of thevariation.

FIG. 3 is a variation of the oven in an open configuration.

FIG. 4 is a variation of the cooking cavity.

FIG. 5 is an example of an oven with a transparent door.

FIG. 6 is an example of dual-panel oven door, including a first panelremovably couplable to a second panel.

FIGS. 7 and 8 are schematic representations of a first and secondvariation of the door with a first and second coupling mechanism,respectively.

FIG. 9 is a schematic representation of a variation of the connectionsbetween oven components.

FIG. 10 is a schematic representation of a variation of the ovenincluding a camera, light emitting elements, and an electricalconnector.

FIG. 11 is a schematic representation of a variation of the ovenincluding a first and second camera.

FIG. 12 is a schematic representation of a variation of the ovenincluding a thermal camera.

FIG. 13 is a schematic representation of airflow through the cookingcavity.

FIG. 14 is an exploded view of a variation of force sensor arrangementwithin the oven.

FIG. 15 is an exploded view of a variation of the user interface unit.

FIG. 16 is a schematic representation of a variation of the system.

FIG. 17 is a schematic representation of the method of oven operation

FIG. 18 is a schematic representation of a variation of the method.

FIG. 19 is a schematic representation of a variation of theidentification module.

FIG. 20 is a schematic representation of a variation of identificationmodule use and updating.

FIG. 21 is an example of the method.

FIGS. 22A and 22B are examples of determining the food size and heightfrom a first and second image, respectively.

FIG. 23 is a schematic representation of a first variation of operation.

FIG. 24 is a schematic representation of a second variation of ovenoperation based on cooking instructions determined from a recipe or fromthe cooking foodstuff, as determined from the measurements.

FIG. 25 is a schematic representation of a variation of automatic ovenoperation adjustment based on measured cooking parameters.

FIG. 26 is a schematic representation of a variation of automated recipegeneration and secondary oven operation based on the generated recipe.

FIG. 27 is a schematic representation of a specific example of dynamiccooking element control.

FIG. 28 is a schematic representation of a specific example of foodstuffidentifier presentation and user confirmation receipt.

FIG. 29 is a schematic representation of various specific examples ofuser device interfaces.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Connected Oven 100.

The connected oven 100 can include a cooking lumen defining a cavity(cooking chamber, cooking volume), a set of heating elements 300, a setof convection elements 400, a processing system 500, a communicationsystem 600, and a set of sensors 700. The oven 100 can additionallyinclude a user interface unit configured to receive instructions fromthe user. The oven 100 functions to cook foodstuff 10 based oninstructions received from a remote system 20. The oven 100 canadditionally function to automatically adjust oven control parameters toachieve a target food parameter (e.g., in real- or near-real time).

1.1 Potential Benefits.

The connected oven 100 can confer several benefits over conventionalovens. First, the connected oven 100 can decrease the physical footprintoccupied by the oven 100 and/or increase the oven's cavity volume(interior volume) to exterior volume ratio. In particular, the connectedoven's interior to exterior volume ratio can be increased overconventional ovens by: eliminating control panel space and replacing theuser interface with a touchscreen 831 and knob; using cone fans, whichcan reduce the required back depth; connecting the power cable to theoven 100 bottom, which can decrease the separation distance between theoven 100 and the wall; and/or including a cool-touch exterior usingthermal insulation mechanisms, which can also decrease wall separationdistance. In a specific example, the oven 100 can have an externalvolume of 13 in by 22 in by 18 in, while having an internal volume of 1cubic foot.

Second, the connected oven 100 can confer increased control over thermaldistribution and/or thermal gradients within the cavity. In particular,the connected oven 100 can be dynamically controlled by a processingsystem 500 (on-board or remote). The processing system 500 candynamically adjust individual convection elements 400, heating elements300, or other oven components to accommodate for cooking parameterdeviations from a target value, create desired thermal profiles withinthe cavity interior, or otherwise selectively control oven operation.

Third, the connected oven 100 can substantially continuously monitor thefoodstuff 10 cooking within the cavity. For example, the connected oven100 can record a video of the cooking foodstuff and stream the video toa remote device (e.g., the server or user device). This can enable auser to share audio and video of the cooking session on a socialnetworking system or any other suitable media sharing system. This canadditionally enable foodstuff analysis. For example, the oven 100,remote system, or other computing system can automatically determine thecooking stage of the foodstuff 10, automatically determine the cookingpreferences (e.g., doneness, crispness, etc.) of the user associatedwith the foodstuff 10 (primary user), automatically determine thecooking preferences of a user population, automatically determine theoven operation parameters or patterns that lead to a given cookingoutcome, automatically control oven operation based on the operationstates of other connected devices, automatically analyze and generatenotifications based on an instantaneous or projected cooking parameter(e.g., estimated time to finish), or automatically determine any othersuitable set of cooking parameter values.

Fourth, the connected oven 100 can facilitate organic, crowdsourcedgroundtruth generation through typical oven use, which can be used torefine the foodstuff 10 analyses modules over time. For example, theconnected oven 100 can prompt users to generate a supervised set oftraining data through typical connected oven use. In one example, theoven 100 can analyze the foodstuff 10 within the cooking cavity 200 andsuggest one or more food classes that the foodstuff 10 potentially fallsinto, based on one or more foodstuff recognition modules. Upon userconfirmation of the recommended food class (or user selection of thefood class for the foodstuff 10), the system can reinforce orrecalibrate the foodstuff recognition modules based on the userselection and data used to initially recognize the foodstuff 10 (e.g.,image). These updated foodstuff recognition modules can then be used forsubsequent foodstuff analysis in one or more ovens, and become morerefined with each instance of oven use.

Fifth, the connected oven 100 can facilitate a more compelling userexperience by performing time-sensitive processes on the oven itself,while performing time-agnostic or long-term analyses on a remotecomputing system. This can have the additional benefit of leveraging thelarger computing power of the remote computing system. The system canconfer this benefit by performing the foodstuff 10 analyses in near-realtime (e.g., recognize the foodstuff 10 within the oven 100 almostimmediately after the foodstuff 10 has been inserted into the oven 100),wherein the oven 100 can recognize the foodstuff 10 using foodstuffrecognition modules stored on-board the oven 100. The system canadditionally or alternatively confer this benefit by including dedicatedhardware (e.g., chipsets) for repetitive tasks. In a specific example,the oven 100 includes a dedicated dewarping chipset that automaticallydewarps or otherwise processes the image recorded by the camera prior toanalysis. The system can additionally or alternatively confer thisbenefit by determining cooking element instructions and controllingcooking element operation at the oven 100 (e.g., based on recipes storedby the oven 100), which can decrease cooking mistakes stemming fromconnection lag. However, the system can otherwise provide a compellinguser experience.

Sixth, the connected oven 100 can be more aesthetically pleasing thanconventional ovens. Some oven variations can include an edge to edgeglass door that functions to provide a larger, clearer cavity viewingarea. The glass can additionally be untinted or otherwise colored tofacilitate clearer viewing. The oven 100 can additionally make thecooking food look better for exterior viewing and/or video recording.For example, the oven 100 can include: adjustable-color orspecific-color cavity lighting (e.g., white, cool white, etc.); a blackcavity background (which can additionally facilitate temperaturecontrol); curved edges, which can confer a more uniform imagingbackground; or control any other suitable visual or audio parameter. Ina specific example, the lighting element can be a set of LEDs, which canconfer the additional benefit of emitting a substantially negligibleamount of heat, thereby enabling finer temperature control. The oven 100can additionally include an aestheticly minimal, single linear wallbumper along the back wall, arranged proximal the center of the bottomedge. The oven 100 can additionally include a notification indicator. Inone variation of the oven 100, the notification indicator (e.g., an LED)illuminates the support surface (e.g., countertop) instead of directinglight at the user. This can result in a less intrusive, friendlier userexperience.

Seventh, the connected oven 100 can be easier to clean than conventionalovens. The connected oven 100 can include a removable tray, a removableinner door wall (e.g., the inner door glass), curved or radiused cavityand bezel corners (wherein the corners can be concave, convex, or haveany other suitable profile), or any other suitable cleaning feature.However, the features of connected oven 100 can confer any othersuitable benefit over conventional ovens.

1.2 Auxiliary Systems.

The oven 100 is preferably used with a remote computing system (remotesystem). The remote system can be a server system, mobile device,desktop, tablet, laptop, or any other suitable remote system. Theservers can be stateless, stateful, or have any other suitableconfiguration or property.

The oven 100 can additionally be used with a user device 30. The userdevice 30 can be a mobile device (e.g., a laptop, tablet, phone,smartwatch, etc.), substantially static device (e.g., desktop,television), or be any other suitable device. The user device caninclude user output (e.g., display, speakers, etc.), user input (e.g.,touchscreen, microphone, etc.), communication module (e.g., wirelesscommunication module), or any other suitable component. The user devicecan be associated with a user device identifier, user account, or anyother suitable identifier for the user device itself or the userassociated with the user device. The user device can be indirectlyconnected to the oven 100 (e.g., through the remote computing system),directly connected to the oven 100 (e.g., through BLE, NFC, WiFi, etc.),or be otherwise connected to the oven 100. The user device cancommunicate with the oven 100 through an application executed by theuser device, such as a native application, browser application, part ofthe operating system, or any other suitable application. The applicationcan have access to user device information (e.g., location, proximity,user account information, user device identifiers, calendars, socialnetworking systems, etc.), beacon information, or any other suitableinformation.

The oven 100 can additionally be used with an auxiliary connectedsystem, such as a temperature control system, lighting system, doorlock, oven 100, or any other suitable auxiliary system. Alternatively,the user device, auxiliary connected systems, or any other suitablesystem can function as the remote computing system, such as in adistributed network system. The oven 100 is preferably wirelesslyconnected to the remote system, user device, auxiliary system, or otherendpoint, but can alternatively be connected to the endpoint via a wiredconnection. In one variation, the oven 100 can be wirelessly connectedto the remote computing system through a long-range connection (e.g.,WiFi, cellular, LTE, etc.), wirelessly connected to a user devicethrough a short-range connection (e.g., NFC, BLE, Bluetooth, etc.),and/or wirelessly connected to an auxiliary system through a meshnetwork. However, the oven 100 can be otherwise connected to any othersuitable endpoint.

1.3 Cooking Lumen.

As shown in FIG. 4, the cooking lumen functions to provide asubstantially thermally insulated environment for cooking foodstuffs(e.g., cooking cavity 200). The cooking lumen is preferablycooperatively defined by an oven body and a door, but can be otherwisedefined. The oven body can be defined by a set of oven walls (top, base,lateral sides, back), wherein the oven walls are preferably planar andmeet at perpendicular junctions (corners). The corners can be angled,radiused (curved, such as convex or concave toward the cooking cavity200), or have any other suitable profile. However, the walls can becurved, wavy, include grooves or fins, or have any other suitableconfiguration, and meet in any other suitable manner. The oven walls canbe made of metal, plastic, glass, ceramic, or any other suitablematerial or combination thereof.

The oven walls can include thermal insulation, such as vacuum insulatedwalls, foam insulation, molded insulation, an airgap between an innerand outer wall, or any other suitable insulation. In one variation, theoven walls can include an internal and external wall cooperativelyforming an airgap therebetween. The oven walls can additionally includeinsulation mounted to the internal and/or external wall, within theairgap. The edges of the walls (e.g., extending between the internal andexternal walls) can be closed (e.g., blind, fluid impermeable, sealed)or open to the cooking cavity 200, adjacent airgap, and/or oven 100exterior (e.g., to form an exhaust or fluid connection). However, theoven walls can be otherwise configured.

In one example, the back wall includes an internal and external wallcooperatively defining an air channel therebetween, wherein the internalwall includes one or more channels extending proximal the oven top 230,oven base 250, and/or oven side 270 walls, such that the air channel isfluidly connected to the cooking cavity 200 through the channels. Inoperation, fans mounted to the back wall can draw air into the air gapthrough the fan, and exhaust the air through the channels back into thecooking cavity 200 (e.g., over the heating elements 300). However,cooking cavity 200 air can be otherwise redirected.

The cavity walls can be black (e.g., matte black), white, have any othersuitable solid color, or have any other suitable combination of colorsand/or patterns. The cavity walls along the cooking lumen canadditionally be reflective (e.g., to facilitate faster preheating timesand more even cooking heat distribution), low-reflectance (e.g., coatedwith a low-reflectance coating), smooth, brushed, rough, or have anyother suitable surface. The oven tray, rack, and/or other ovencomponents can have the same color and/or surface texture, which canfacilitate faster image processing for foodstuff 10 analysis.

The door of the cooking lumen functions to actuate relative to andtransiently seal against the oven body to form a substantially thermallyinsulated cavity. The door is preferably transparent (e.g., opticallyconnecting the ambient environment with the cooking cavity 200), but canbe translucent, opaque, or have any other optical property. The door ispreferably substantially planar and seals against the edges of the top,bottom, and lateral side walls, but can have any other suitableconfiguration and otherwise couple to the oven walls. The door can bemade of glass, plastic, metal (e.g., perforated metal, solid metal), orany other suitable material or combination thereof. In a specificexample, the entirety of the door is glass except for a metal bezelaround the door edge, wherein the metal bezel does not substantiallyimpinge upon the broad face of the door. The door can additionallyinclude thermal insulation, such as vacuum insulated walls, foaminsulation, an air gap cooperatively defined between an inner and outerdoor wall, or any other suitable insulation. The air gap canadditionally function to thermally insulate components on the outer doorwall from radiation (e.g., heat) within the cavity. The door can bemounted to the oven body along a lateral edge, a longitudinal edge, oralong any other suitable edge.

In one variation, examples shown in FIG. 6, FIG. 7, FIG. 8, the door isa dual-panel door, and includes an inner and outer panel aligned inparallel (e.g., wherein the outer panel 214 defines a first broad faceand a first normal vector, wherein the inner panel 212 defines a secondbroad face and a second normal vector, and wherein the outer panel 214is coupled to the inner panel 212 with the first and second normalvectors substantially coaxially aligned) and offset by an air gap. Theair gap can function as thermal insulation for the user input 830 (e.g.,the touchscreen), a thermal path for component cooling, or perform anyother suitable functionality. The inner panel 212 can be removablycoupled to the outer door wall to enable inner wall removal forcleaning. The inner panel 212 can be coupled to the outer panel 214 by acoupling mechanism 216. The coupling mechanism 216 can be arranged atthe corners of the door, along a first and second edge of the door(opposing or adjacent), along a portion of the bezel, along the broadface of the door, or along any other suitable portion of the door. Thecoupling mechanism can be clips, magnetic elements, screws, a tongue andgroove system (e.g., wherein the inner door wall slides into a groovedefined by the bezel of the outer door wall), or be any other suitablecoupling mechanism. The oven 100 can include one or more couplingmechanisms of same or different type, evenly or unevenly distributedalong the door (e.g., along the door perimeter). In one example, theinner panel 212 includes a magnetic element (e.g., a magnetic bezel)that magnetically couples to the interior surface of the outer panel 214and offsets the inner panel 212 from the outer panel 214. The outerpanel 214 can additionally include a bezel extending along the outerpanel 214 perimeter, wherein the inner panel 212 bezel can couple to theouter panel 214 along the outer panel bezel edge, to the outer panel 214broad surface within the outer panel bezel, or to the outer panel 214broad surface outside the outer panel bezel. However, the inner panel212 can be substantially permanently mounted to the outer panel 214(e.g., welded, formed as a singular piece, etc.) or be otherwise coupledto the outer door wall. However, the door can be otherwise constructed.

The inner and outer panel can both include transparent windows, whereinthe transparent windows are preferably aligned to cooperatively form atransparent window for the door. However, the respective transparentwindows can be offset or otherwise arranged. The transparent windows(e.g., transparent regions) can be coextensive with the cooking cavity200, but can alternatively be larger or smaller. In one example, theinner and outer panels both include edge-to-edge glass. The visualtransmittance of the inner and outer panels are preferably substantiallyequal (e.g., transmit 100%, 90%, 80%, 50%, or any other suitableproportion of the incident radiation), but can alternatively bedifferent (e.g., wherein the inner and/or outer panels can be tinted todifferent degrees). The reflectivity of the inner and outer panels arepreferably substantially similar (e.g., low, high, etc.), but canalternatively be different. The refraction index of the first and/orsecond panel is preferably low (e.g., with a refractive index of 1, 1.5,2, etc.), but can alternatively be high (e.g., refraction index of 3, 4,5, etc.) or have any other suitable value. All or a portion of the innerand/or outer panels can additionally include anti-glare coatings,reflective coatings (e.g., to reflect light in the visual spectrum, inthe IR spectrum, in the UV spectrum, in the microwave spectrum, etc.),or any other suitable type of coating or treatment embedded within thepanel, on a panel interior surface, or on a panel exterior surface. Inone variation, all or a portion (e.g., center, edges, etc.) of the innerand/or outer panels can include a coating that minimizes infraredtransmission (e.g., out of the cooking cavity). The coating can be IRreflective or have any other suitable characteristic. The coating can bemade of metal oxide (e.g., tin oxide, zinc oxide, cadmium stannate,Fe₂O₃, Cr₂O₃, ZnS, Sb₂O₃, ZrO₂, etc.), polymer (e.g, PVDF), sol-gel, ormade of any other suitable material. However, the inner and outer panelscan be otherwise constructed, arranged, and/or have any other suitableproperty or feature.

1.4 Heating Elements.

As shown in FIG. 4, the heating elements 300 of the oven 100 function toheat portions of the cavity. The heating elements 300 can additionallyor alternatively function as cooling elements. The heating elements 300can be resistive, chemical, Peltier, inductive, or utilize any othersuitable heating mechanism. The heating elements 300 can be metal,ceramic, carbon fiber, composite (e.g., tubular sheathed heatingelement, screen-printed metal-ceramic tracks, etc.), combination (e.g.,thick film technology, etc.), or be any other suitable type of heatingelement. The oven 100 can include one or more heating elements 300(e.g., 4, 6, 8, etc.), wherein all heating elements 300 are preferablysubstantially identical but can alternatively be different. Each heatingelement can be individually addressed and controlled, controlled as asubset (e.g., controlled with a second heating element), controlled as apopulation (e.g., wherein all heating elements 300 are controlledtogether), or controlled in any other suitable manner. The heatingelement(s) are preferably controlled by the processor, but can becontrolled by a secondary on-board processor, remotely controlled by theuser device or remote system, or be otherwise controlled.

The heating elements 300 can be linear, curved (e.g., boustrophedonic),circular, or otherwise configured. The heating element can be arrangedalong the oven 100 bottom, sides, top, back, front (e.g., in the doorbezel), corners, and/or along any other suitable cavity surface, or bearranged proximal but offset from the respective cavity surface. In oneexample, the oven 100 can include a heating element proximal aconvection element (e.g., fan), wherein the heating element cansuperheat the air circulated by the convection element. In a specificexample, the oven 100 can include a circular heating element concentricwith the fan. The heating elements 300 are preferably substantiallysymmetrically distributed along the respective cavity surface, but canalternatively or additionally be evenly distributed, unevenlydistributed, asymmetrically distributed, or otherwise distributed alongthe cavity surface. The heating elements 300 can be raised above thecavity surface, arranged behind the cavity surface (e.g., distal thecavity across the cavity surface, within the wall, etc.), be flush withcavity surface (e.g., integrated into the cavity surface), arrangedproximal the sensors or light emitting elements (e.g., bracket thesensors and/or light emitting elements), arranged proximal one or moresurfaces (e.g., oven top 230, oven 100 bottom, oven sides 270, etc.), orbe otherwise arranged. The heating elements 300 can be staticallymounted to the cavity surface, removably mounted to the cavity surface(e.g., by clips, pins, magnets, etc.), or be otherwise coupled to theoven 100. The heating elements 300 can be covered by a heatsink orthermal diffuser (e.g., a thermally conductive sheet), which canfunction to more evenly distribute heat within the cooking cavity 200.Alternatively, the heating elements 300 can remain uncovered. In onespecific example, the oven 100 can include six linear heating elements300, three arranged lengthwise along the oven base 250 and threearranged lengthwise along the oven top 230, wherein the heating elements300 are substantially evenly distributed along the cavity depth.

1.5 Convection Elements.

The convection elements 400 of the oven 100 function to distribute heatabout the foodstuff 10 and/or within the cavity. The convection elements400 can additionally function to cool sensor components (e.g., thecamera, LED, etc.), cool the cavity (e.g., exhaust air out of the oven100), or otherwise move fluid within the cavity, into the cavity, or outof the cavity. Alternatively, the oven 100 can be an impingement oven(e.g., wherein the convection elements 400 blow hot air into thecavity), a radiation oven (e.g., wherein the oven 100 lacks convectionelements 400 and/or facilitate natural convection), or be any othersuitable type of oven 100. The convection elements 400 can be fans,Peltier elements, chemical reactions, fins, air manifolds, pumps, or anyother suitable mechanism capable of moving fluid. The convectionelements 400 can be active or passive. The fan can be an axial flow fan,centrifugal fan, cross-flow fan, or any other suitable fan. The fan canbe a cone fan, arranged with the apex proximal the cavity interior; abox fan; or be any other suitable type of fan. The fan can be the samethickness as the oven wall (e.g., single wall, composite wall with aninner and outer wall, etc.), be thicker than the oven wall, or bethinner than the oven wall. In one example, as shown in FIGS. 1 and 2,the oven wall can include an inner and outer wall cooperatively definingan air manifold therebetween. The fan can be mounted to the inner wall,and be fluidly connected to the air manifold at one end and the cavityat the other. The fan can be thicker than the inner wall, and extendinto the cavity. Alternatively, the fan can extend outward of the oven100, be thinner than the air manifold, or have any other suitableconfiguration.

The convection element can be arranged along the back wall (as shown inFIG. 4), side wall(s), top, bottom, front, or any other suitable portionof the cavity. The oven 100 can include one or more convection elements400, wherein all convection elements 400 are preferably substantiallyidentical but can alternatively be different. Each convection elementcan be individually controlled, controlled as a subset (e.g., controlledwith a second heating element), controlled as a population (e.g.,wherein all heating elements 300 are controlled together), or controlledin any other suitable manner. The convection element(s) are preferablycontrolled by the processor, but can be controlled by a secondaryon-board processor, remotely controlled by the user device or remotesystem, or be otherwise controlled. The convection elements 400 arepreferably symmetrically arranged about the cavity, but canalternatively or additionally be evenly distributed, unevenlydistributed, asymmetrically distributed, or otherwise distributed aboutthe cavity. The convection elements 400 can be raised above the cavitysurface (e.g., be arranged within the cavity), arranged behind thecavity surface (e.g., distal the cavity across the cavity surface,within the wall, etc.), be flush with cavity surface (e.g., integratedinto the cavity surface), or be otherwise arranged. The convectionelements 400 can be statically mounted to the cavity surface, removablymounted to the cavity surface (e.g., by clips, pins, magnets, etc.), orbe otherwise coupled to the oven 100. In one specific example, the oven100 can include two convection elements 400, each arranged on the backcavity wall, substantially equidistant from a lateral (e.g., vertical)back cavity wall axis. However, the convection elements 400 can beotherwise arranged. The oven 100 can additionally or alternativelyinclude air manifolds, air inlets, air outlets, vents, or any othersuitable fluid aperture to facilitate airflow within the cavity.

1.6 Processing System 500.

The processing system 500 of the oven 100 functions to record sensormeasurements, process sensor measurements, control communication betweenthe oven 100 and secondary system (e.g., remote system, user device,etc.), control oven component operation based on recipe instructions,and/or control oven component operation based on user input received atthe user interface unit. The processing system 500 preferablyindividually controls the oven components (e.g., wherein the componentsare individually indexed), but can alternatively control like componentstogether. The processing system 500 can additionally or alternativelyfunction to automatically determine a classification for foodstuff 10within the cooking cavity 200 (e.g., based on an foodstuff 10 featuresextracted from sensor measurements), automatically oven componentoperation based on the classification, or perform any other suitablefunctionality. The processing system 500 can be the same as that drivingthe user interface unit, or be a separate and distinct processorconnected to that driving the user interface unit. The processing system500 can include one or more processors, wherein the processor can be aPCB, CPU, GPU, or any other suitable processing or computing system.

The processing system 500 can additionally include one or more pieces ofcomputer memory (e.g., non-volatile memory, such as RAM or Flash;volatile memory, etc.) that functions to store: a set of recipes foreach of a plurality of food classes, image processing modules (e.g., oneor more: segmentation modules, identification modules, pixel-to-physicalarea maps, etc.), foodstuff analysis modules (e.g., time-to-finishestimation modules, cooking element control instruction adjustmentmodules, etc.), user preferences, foodstuff 10 history, or any othersuitable data. The data stored by the computer memory can be static,updated by the processing system 500 (e.g., in response to receipt ofupdated data from the remote computing system), or otherwise changed atany other suitable frequency.

The processing system 500 is preferably electrically or wirelesslyconnected to every active oven component (e.g., the heating elements300, convection elements 400, user inputs, display, sensors 700, lightemitting element, memory, electrical connectors, etc.), but canalternatively be connected to a subset of the active oven components. Inone example, the processing system 500 is connected to the active ovencomponents over a controller area network (CAN) bus. The processingsystem 500 can be mounted to the oven body (e.g., top, base, walls,etc.), door, or any other suitable oven component. When the processor ismounted to the door, the bus (e.g., cable, wires, pins, etc.) can extendalong the door broad face (e.g., along the bezel) to connect to theremainder of the active oven components. However, the processor can beotherwise physically connected to the active oven components. Theprocessor can be part of the user interface unit or be a separatecomponent. In one variation, the processor is mounted to the display810. In a second variation, the processor is mounted to the oven 100with a processor normal vector intersecting the cooking cavity 200. In athird version, the processor is mounted to the knob. However, theprocessor can be otherwise mounted.

1.7 Communication System.

The communication system 600 of the oven 100 functions to receive and/ortransmit information. The communication system 600 can be electricallyconnected to the processor or oven components, wirelessly connected tothe processor or oven components, or be otherwise connected to theprocessor or oven components. The communication system 600 can be wiredor wireless. The communication system 600 can be WiFi, cellular, Zigbee,Z-wave, NFC, BLE, RF, mesh, radio, or any other suitable communicationsystem. The communication system 600 can include a transmitter, areceiver, or both. The communication system 600 can include one or moreantennas. The antenna 610 can extend along the user interface unit, theprocessor, an oven wall, a door bezel, or along any other suitablecomponent. The oven 100 can include one or more communication systems600, wherein multiple communication systems 600 can be the same ordifferent. The communication system 600 can be part of the userinterface unit or be a separate component. The communication system 600is preferably arranged within an oven wall, but can alternatively bearranged along a wall or door, within a wall or door, within the cavity,or in any other suitable location. The communication system 600 ispreferably arranged along an outer wall, but can alternatively bearranged along an inner wall or along any other suitable portion of thecommunication system 600. The communication system 600 can be mounteddirectly to the oven 100, be mounted to the processor, be mounted to thedisplay 810, be mounted to a user input, or be mounted to any othersuitable oven component. In one variation, the communication system 600is mounted to the oven 100 with a communication system normal vectorintersecting the cooking cavity 200.

1.8 Sensors.

The sensors 700 of the oven 100 function to record cooking parameters.More specifically, the sensors function to monitor oven operationparameters and/or foodstuff parameters. The sensors 700 can includeoptical sensor 710 (e.g., image sensors, light sensors, etc.), audiosensors, temperature sensors, volatile compound sensors, weight sensors,humidity sensors, depth sensors, location sensors, inertial sensors(e.g., accelerators, gyroscope, magnetometer, etc.), impedance sensors(e.g., to measure bio-impedance of foodstuff), hygrometers, insertiontemperature sensors (e.g., probes), cooking cavity 200 temperaturesensors, timers, gas analyzers, pressure sensors, flow sensors, doorsensors (e.g., a switch coupled to the door, etc.), power sensors (e.g.,Hall effect sensors), or any other suitable sensor. The sensors can bedirectly or indirectly coupled to the cooking cavity 200. The sensorscan be connected to and controlled by the processor, or be otherwisecontrolled. The sensors are preferably individually indexed andindividually controlled, but can alternatively be controlled togetherwith other like sensors.

In one variation, the sensor 700 can include an optical sensor 710 thatfunctions to measure optical data about the cooking cavity 200 (e.g.,foodstuff 10 within the cooking cavity 200). In a first embodiment, thesensor includes a camera configured to record images 11 or video of thecooking cavity 200 (e.g., food cooking within the cavity). The cameracan be a CCD camera, stereocamera, hyperspectral camera, multispectralcamera, video camera, wide angle camera (e.g., a fisheye camera with afisheye lens, a rectilinear camera with a rectilinear lens, etc.) or anyother suitable type of camera. In a specific example, the wide-anglecamera can have an approximately 180-degree field of view (e.g., within10 degrees or less). The camera can be cooled by the convection elements400, cooled by a separate cooling system (e.g., a radiator and fan,watercooling, etc.), or remain uncooled. The camera can record images 11using radiation emitted or reflected by the heating elements 300, by thefoodstuff 10, by the oven walls, by an emitter, or by any other suitableradiation source. Alternatively or additionally, the camera can recordimages using ambient light.

The camera can additionally include dedicated processing hardware 711that pre-processes the captured image 11. Examples of dedicatedprocessing hardware include: dewarping hardware (e.g., to correct thedistortion an image captured by a fisheye camera), mosaicing hardware(e.g., to stitch together multiple images recorded by one or morecameras into a single image, virtual 3D model, etc.), resizing hardware,filtering hardware, hardware that identifies image elements (e.g.,identify a reference point, identify recurrent image elements, such as arack), or any other suitable hardware. The dedicated processing hardwarecan be a generic chipset with the algorithm flashed onto the chipset(e.g., read-only), be a chipset with dedicated processing circuitry(e.g., wherein the algorithm is determined by the circuitry), or be anyother suitable hardware. The dedicated processing hardware is preferablyconnected between the camera and processor in series, but canalternatively be connected in parallel or be otherwise connected.

The camera and/or any associated processing systems (e.g., chipsets) canbe arranged along the top of the cavity (e.g., distal the heatingelements 300, distal the feet, etc.), arranged along the side of thecavity, arranged along the bottom of the cavity, arranged in a corner ofthe cavity (e.g., upper right, upper left, etc.), arranged in the doorof the cavity (e.g., supported by the inner door wall, supported by theouter door wall, be integrated into the user interaction unit, etc.), orbe supported by any other suitable portion of the oven 100.Alternatively, the associated processing systems can be arrangedseparate from the camera (e.g., be part of the processing system, etc.).The camera lens is preferably flush with the cavity wall, but canalternatively be recessed or protrude from the cavity wall. The cameracan be centered along the respective oven surface, offset from the ovensurface center, or be arranged in any other suitable position. Thecamera can be statically mounted to the oven surface, movably mounted tothe oven surface (e.g., rotate about a rotational axis, slide along asliding axis, etc.), or be otherwise coupled to the oven 100. The oven100 preferably includes one or more video cameras. The cameras can besubstantially identical or be different. The cameras can be evenlydistributed throughout the cavity (e.g., symmetrically distributed), orbe unevenly distributed.

The camera can have a constant frame rate, variable frame rate, or anyother suitable frame rate. In one variation, the frame rate can bedynamically adjusted to accommodate for the radiation from the foodstuff10, ambient light, internally emitted light, or any other suitablelight. The camera can be statically mounted to the oven 100, actuatablymounted to the oven 100 (e.g., rotate about an axis parallel to an oven100 longitudinal axis, lateral axis, multi-direction, etc.), orotherwise mounted to the oven 100. The camera can dynamically apply oneor more filters to single out a given set of light bands. The camera candynamically apply one or more lenses to adjust the camera field of viewor any other suitable optical parameter. The camera can additionallyinclude a set of mirrors that selectively redirect radiation (e.g.,light) or images to the foodstuff 10 and/or camera. The camera can havea static field of view, variable field of view, or other suitable fieldof view. The camera is preferably arranged with its field of view (FOV)directed at the cavity, but can alternatively be otherwise arranged. TheFOV (single or combined) preferably substantially encompasses theentirety of the cavity, but can alternatively encompass a subset of thecavity or encompass any other suitable portion of the cavity. The FOVpreferably encompasses at least the food tray or bottom of the cavity,but can additionally or alternatively encompass the front, back, walls,top, or any other suitable portion of the cavity. The camera ispreferably sensitive to (e.g., measure in the spectral wavelength of)visual light, but can alternatively or additionally be sensitive toinfrared light, ultraviolet light, or any other suitable electromagneticwavelength.

In a first variation, as shown in FIG. 6, the oven 100 includes a singlecamera mounted to the top of the cavity and directed with the FOV towardthe cavity bottom. In a second variation, the oven 100 includes a singlecamera of limited view (e.g., wherein the FOV is less than a majority ofthe cavity), wherein the camera is directed toward a food pan (e.g.,tray) proximal the heating elements 300. In a third variation as shownin FIG. 5, the oven 100 includes a first and second camera havingdifferent FOVs (e.g., arranged along different sides of the oven 100 anddirected in opposing directions) directed at the food pan. In thisvariation, a virtual 3D model can be constructed from the imagesrecorded by the first and second cameras. However, the oven 100 caninclude any other suitable camera.

In a second embodiment, the optical sensor 710 can include one or morephotodiodes. The photodiodes can be used to determine the temperature ofthe foodstuff 10, determine the ambient light (e.g., be an ambient lightsensor 770), determine the temperature of the cavity or cavitysub-volume, determine the cooking cavity light intensity, or measure anyother suitable light parameter. In one variation, the photodiodemeasurement can be used to calibrate and/or control the heating elementand/or convection elements 400. In a second variation, the photodiodemeasurement can be used to calibrate the image recorded by the camera.In a third variation, the photodiode can be used to dynamically adjustthe touchscreen 831 backlight. In a fourth variation, the photodiodemeasurements can be used to dynamically adjust the internal cavity light(e.g., by the processor) based on the ambient light to account forambient light leaking into the cooking cavity 200 (e.g., tocooperatively meet a target cooking cavity light intensity, etc.). In afifth variation, the photodiode measurements can be used to dynamicallyadjust the internal cavity light (e.g., by the processor) based on theambient light to minimize a difference between the internal cavity lightand ambient light. However, the photodiode measurements can be otherwiseused. The oven 100 can include one or more photodiodes. The photodiodeis preferably mounted to the oven 100 exterior (e.g., the oven door 210,oven sides 270, oven top 230, etc.), but can alternatively oradditionally be mounted to the oven 100 interior or to any othersuitable portion of the oven 100.

In a second variation, the sensor 700 can include a 3D scanner thatfunctions to scan the cooking cavity 200. The 3D scanner is preferably anon-contact scanner, but can alternatively be any other suitablescanner. Examples of 3D scanners include time-of-flight scanners,triangulation scanners, conoscopic holography, optical rangefinding,radar, sonar, or any other suitable scanner capable of collecting dataon the shape and contours of the material within the cooking cavity 200.

In a third variation, the sensor 700 can include one or more forcesensors 750 (e.g., weight sensors). The force sensors 750 can functionto measure the weight of the foodstuff 10 before, during, and after thecooking session. The force sensor measurements can additionally be usedto determine the foodstuff weight change throughout the cooking session,which can be used to determine the cooking stage of the foodstuff 10.The force sensors 750 can be arranged in the tray supports (e.g.,grooves supporting the food tray), rack supports (e.g., groovessupporting the food rack), in the rack or tray itself, in the oven feet251 (e.g., the oven 100 standoff from the support surface, mounted tothe oven base 250 along a surface opposing the cooking cavity 200,etc.), between the oven feet 251 and the oven base 250, along the oventop 230, or in any other suitable location. In a specific example, theoven 100 includes a single weight sensor arranged in a single foot. In asecond specific example, the oven 100 includes a weight sensor arrangedin each foot (e.g., wherein the oven 100 can include four feet). In thesecond specific example, the oven 100 can additionally automaticallydetermine the foodstuff mass distribution within the cooking cavity 200based on the measured weight distribution across the feet.

In a fourth variation, the sensor can include one or more acousticsensors that records sounds within the cavity. More preferably, thesensor records sounds of the food cooking. The sound record can beanalyzed (at the oven 100 or remotely) to categorize the foodstuff 10,determine the cooking stage, or determine any other suitable foodstuffparameter. The sound sensor can be a microphone, transducer, ultrasoundreceiver, or be any other suitable sensor. The sound sensor canadditionally include an emitter (e.g., speaker) configured to emitsounds of a predetermined frequency (e.g., ultrasound, alerts, etc.) andamplitude. In one variation, a first sound sensor can be arranged nextto the convection elements 400 and a second sound sensor can be arrangeddistal the convection elements 400, wherein measurements from the firstsound sensor can be used to cancel fan noise from the measurements ofthe second sound sensor. In a second variation, the system can includean external speaker voice commands, internal speaker→foodstuff cookingparameter (sizzle, pop)

However, the sound sensor can be otherwise arranged and/or used.

1.9 Emitters.

The oven 100 can include one or more emitters 730 that functions to emitsignals that the sensors can measure. For example, the emitter 730 canbe a light emitter 731, wherein the camera records optical or visualimages using light or other electromagnetic radiation emitted by thelight emitter. In a second example, the emitter can be an acousticemitter, wherein the acoustic sensor records acoustic images usingacoustic waves emitted by the acoustic emitter. However, the emitter canemit any other suitable signal. The oven 100 can include one or moreemitters of same or different type. Multiple emitters can beindividually indexed and individually controlled, controlled together,or otherwise controlled by the processing system 500. Multiple emitterscan be arranged in an array or in any other suitable configuration.Multiple emitters can be substantially evenly distributed within thecavity (e.g., along a cavity axis, about the optical sensor 710, etc.),or be unevenly distributed. The emitter(s) are preferably mounted to thecooking lumen (e.g., interior walls), but can alternatively be mountedto the oven 100 exterior, to an exterior arm aimed at the cooking cavity200, or be otherwise arranged.

In one variation, the emitter includes a light emitter. The lightemitter of the oven 100 functions to illuminate the cooking cavity 200,wherein the optical sensor 710 (e.g., image sensor) records lightreflected off content within the cooking cavity 200 (e.g., foodstuff10). The emitter (e.g., light emitting element) can be directed at thesame volume as the sensor (camera), be directed at a different volume,or be arranged in any other suitable manner. The emitter is preferablymounted to the same wall as the optical sensor 710 (e.g., to the oventop 230), but can alternatively be mounted to a different wall (e.g., anadjacent wall, etc.) or to any other suitable surface. The emitter ispreferably flush with the cavity wall, but can alternatively be recessedor protrude from the cavity wall.

The emitter can emit light having constant color temperature,saturation, color, or any other suitable parameter, or be substantiallyadjustable (e.g., by the processor, etc.). The light can be selected tomaximize the aesthetics of the cooking foodstuff. The emitter can emitwhite light (e.g., cool white light, warm white light, etc.), light inthe visible spectrum, IR light, UV light, or light having any othersuitable wavelength. The emitted light intensity can be adjusted basedon the ambient light (e.g., increased when the total cavity light fallsbelow a threshold intensity, such as below 10 W/m2, 5 candelas, etc.),based on the foodstuff 10 (e.g., increased for dark foods, decreased forlight-colored foods), based on any other suitable parameter, or besubstantially fixed. In one example, the oven 100 includes a first andsecond light emitting element arranged on a first and second side of thecamera, respectively, wherein the first and second light emittingelements are equidistant from the camera. Examples of the emitterinclude an LED, OLED, incandescent lightbulb, or any other suitableelectromagnetic radiation emitter.

1.10 User Interface Unit.

As shown in FIG. 1, the oven 100 can additionally include a userinterface unit 800 (control unit) that functions to receive input from auser. The user interface unit 800 can additionally function to presentinformation to a user. The user interface unit 800 can include a display810 and a user input 830. The user interface unit can additionallyinclude a second user input, the processing system 500, computer memory,the communication system 600, or any other suitable component. The userinterface unit can be electrically connected to the processor (e.g.,through a wire running through the door frame), wirelessly connected tothe processor (e.g., through BLE, NFC, RF, or any other suitablewireless connection), or otherwise connected to the processor.

The user interface unit 800 is preferably a single unit, and ispreferably substantially collocated (e.g., with components overlayingone another; with each component mounted to one or more of the remainderof the interface unit components, etc.), but can alternatively bedistributed over the oven body. All or part of the user interface unitcomponents can be substantially planar, and define a broad face and/or anormal vector (e.g., to the broad face). However, the user interfaceunit components can be otherwise configured. All or part of the userinterface unit can be mounted to the door, more preferably the outerpanel 214 but alternatively the inner panel 212, but can alternativelyor additionally be mounted to the oven top 230, base 250, sides 270, orany other suitable portion of the oven 100. All or part of the userinterface unit 800 is preferably mounted to the transparent window ofthe door 210, but can alternatively be mounted to the door bezel, doorhandle, or to any other suitable portion of the oven. All or part of theuser interface unit can be coextensive with the transparent window,occupy a segment of the door, or have any other suitable dimension. Allor part of the user interface unit can be smaller than the transparentwindow, and be arranged such that the door regions adjacent (e.g.,contiguous) the user interface unit (e.g., along one, two, three, or alluser interface unit edges) are transparent. In one example, the userinterface unit can be arranged along the transparent window, offset fromthe door bezel (e.g., door edge, panel edge) or inner panel couplingregion. However, the user interface unit can abut the door bezel or beotherwise arranged. All or part of the user interface unit is preferablymounted with the broad face parallel the door and the normal vector orprojection intersecting the cooking cavity 200, but can alternatively bemounted offset from the cooking cavity 200 or mounted in any othersuitable orientation.

The display 810 of the user interface unit 800 functions to presentinformation to a user. The display 810 can be the oven door 210, aportion of the oven door 210, be a separate component integrated intothe door (e.g., be flush with the door exterior and/or interior; extendoutward of the door exterior and/or interior), be a separate anddiscrete component from the oven door 210, or be otherwise arranged.When the display 810 is a separate component integrated into the door,the display 810 can be mounted to a recess (e.g., thinned portion)defined in the door, mounted to the door surface (e.g., to afull-thickness portion of the door), mounted to a cutout in the door(e.g., wherein the display 810 is mounted within the cavity defined bythe cutout), or otherwise mounted to the door. In one variation, thedisplay 810 can occupy the entirety of the door. In a second variation,the display 810 can occupy a segment of the door. In an embodiment ofthe second variation, the display 810 is mounted to the transparentwindow of the door.

The display 810 is preferably arranged along (e.g., mounted to) theouter door wall, such that the airgap between the inner and outer doorwalls function to thermally insulate the display 810 from the cavityinterior, but can alternatively be arranged along the inner door wall,arranged along a side or the top of the oven 100, or arranged in anyother suitable location. The display 810 can be transparent, opaque,translucent, or have any suitable optical parameter. In one variation,the display 810 can be opaque, but include a camera along the side ofthe display 810 proximal the cavity that streams of the cooking foodbehind the display 810 to be displayed on the display 810. The display810 can be a liquid crystal display (LCD), a thin film transistor liquidcrystal display (TFT-LCD), light emitting diode display (LED), anorganic light emitting diode (OLED) display, a plasma display, a videoprojector, a combination thereof, plasma display, projection display, orbe any other suitable display. The OLED display can be a passive-matrixOLED (PMOLED) or active-matrix OLED (AMOLED) display. However, the userinterface unit can include any other suitable display configured in anyother suitable manner.

The user input 830 of the user interface unit functions to receive inputfrom the user, which can be communicated to the processor of the oven100. The user input 830 can be arranged proximal the display 810 (e.g.,overlaid over the display 810, arranged next to the display 810, etc.),arranged distal the display 810, or be otherwise arranged. The userinput 830 can include a touchscreen 831 overlaid over the display 810,the user device, a keypad (digital or physical), a knob, or any othersuitable user input. The touchscreen 831 can be a capacitivetouchscreen, such as a surface capacitance, mutual capacitance,projected capacitance, or self capacitance touchscreen; resistivetouchscreen; dynamic touchscreen defining tixels; surface acoustic wavetouchscreen; infrared grid; acoustic pulse touchscreen; or any othersuitable user input capable of recognizing one or more touches. In onevariation, the capacitive touchscreen can be formed from the door (e.g.,functioning as the touchscreen insulator) and a transparent conductorcoupled to the door (e.g., etched into, coating, etc. the interior orexterior surface of the door or outer panel 214). In a second variation,the capacitive touchscreen can include an insulator coated with aconductor, wherein the capacitive touchscreen is integrated into thedoor. However, the touchscreen 831 can be otherwise configured.

The user interface unit can additionally include a second user input.The second user input can function to change the operation mode of thefirst user input, function to enable a rougher degree of control overthe user interface, or enable any other suitable functionality. Thesecond user input can be a rotary knob 833 (e.g., click knob), scrollwheel, touchscreen, or be any other suitable user input. The rotary knob833 can be magnetically indexed to a plurality of angular positions,actuate about a rotational axis, actuate along the rotational axis(e.g., be depressable), or be otherwise actuatable. In one example, knobrotation can move through a set of lists, while touchscreen interaction(e.g., swiping) can move through a set of content within a given list.Knob depression or touchscreen tapping can select an item within a list.However, any other suitable user input interaction can interact with theuser interface. The second user input is preferably mounted proximal thefirst user input, but can alternatively be mounted distal the first userinput. The second user input can be mounted to the oven door 210, ovenbody, or any other suitable mounting point. The second user input can bemounted with a major axis (e.g., rotary axis, normal axis, etc.) orprojection intersecting the cooking cavity 200, offset from the cookingcavity 200, or otherwise arranged.

1.11 Auxiliary Components.

The oven 100 can additionally include auxiliary component interfaces,which function to expand the functionality of the oven 100. For example,the oven 100 can include an electrical connector 101 (e.g., temperatureprobe jack for an insertion temperature probe) that functions toelectrically connect to (e.g., receive data from and/or supply power to)a temperature probe. In a second variation, the oven 100 can include aUSB connector for an auxiliary weight scale, food scanner, or otherconnected kitchen device (e.g., sous vide system). However, the oven 100can include any other suitable interface and utilize the information inany other suitable manner. The auxiliary component interfaces arepreferably mounted to the cooking lumen (e.g., oven body interior), butcan alternatively or additionally be mounted to the oven 100 exterior.The auxiliary component interfaces are preferably connected to theprocessor (e.g., through a wired or wireless connection), but canalternatively be connected to any other suitable component.

The oven 100 can additionally include a wall standoff 291 (bumper), asshown in FIG. 2, that functions to maintain a minimum distance betweenthe oven 100 and a vertical member (e.g., wall). The oven 100 caninclude one or more wall standoffs. The wall standoff preferablyincludes at least two points extending equidistant from an oven surface,but can alternatively extend different distances from the oven surface.The two points are preferably substantially symmetric about an oven axis(e.g., the vertical or longitudinal axis), but can alternatively beasymmetric or otherwise arranged. The two points can be substantiallyhorizontally aligned, offset, or otherwise arranged. In one variation,the wall standoff is a singular linear piece, aligned along the centerof the bottom edge of the oven back wall 290 (external wall). In asecond variation, the wall standoff includes a first and secondhorizontal linear piece arranged along a first and second side of thebottom edge of the oven back wall 290. However, the wall standoff can beangled, vertical, or otherwise oriented.

The oven 100 can additionally include a fluid injector that functions toselectively inject a predetermined volume of fluid into the cookingcavity 200. The fluid injector can include a fluid reservoir, a fluidmanifold fluidly connecting the reservoir to the cooking cavity 200, anda valve system controlling fluid flow between the reservoir and cookingcavity 200. The fluid injector can additionally include a heatingelement that functions to heat fluid from the reservoir prior to fluidinjection into the cooking cavity 200 (e.g., to a predeterminedtemperature). Alternatively, the fluid can be heated by waste heat fromthe cooking cavity 200, remain unheated, be cooled, or be otherwisetreated. The fluid injector can additionally include a nozzle (e.g.,static or actuatable) that functions to direct fluid introduction intothe cooking cavity 200. Examples of the fluid injector include a steaminjector, flavor injector (e.g., smoke injector, etc.), or any othersuitable injector.

The oven 100 can additionally include a catalytic converter thatfunctions to remove odors from the oven exhaust. The catalytic converteris preferably arranged in-line (e.g., serially) with the oven 100exhaust, but can alternatively be arranged within the cooking cavity 200or along any other suitable portion of the oven 100. The oven 100 canadditionally include a cooling system (e.g., Peltier cooler, watercooling system, heat pump, etc.), or include any other suitablecomponent.

The oven 100 can additionally include a power cable that functions tosupply power from a power grid or wall outlet to the oven 100. The powercable can additionally communicate data or perform any other suitablefunctionality. The power cable can extend from the bottom of the oven100, from the back of the oven 100, from the side of the oven 100, orfrom any other suitable portion of the oven 100. The power cable can beround, flat, or have any other suitable form factor.

The oven 100 can additionally include a food tray (e.g., baking tray,baking pan) that functions to support foodstuff within the cookingcavity. The food tray can additionally function to provide asubstantially consistent background for food imaging and/or imageprocessing. The food tray can be metal (e.g., aluminum), ceramic, or bemade of any other suitable material. The food tray is preferably matte,but can alternatively be reflective. The food tray is preferably black(e.g., on the food-supporting broad face, the opposing broad face,etc.), but can alternatively have any other suitable color. The foodtray preferably defines a non-stick surface, which can be an inherentproperty of the food tray material, created by a coating, or otherwiseobtained. The coating can include Teflon™, ceramic, or any othersuitable material. In a specific example, the tray is made of solidaluminum with a non-stick coating. The aluminum can minimize warpingunder heat gradients (e.g., due to the rapid heat distributionproperties), and the non-stick coating can additionally prevent thealuminum from reacting with acids.

2. Applications/Smart Cooking Method.

The method for connected oven operation can include: recording a set ofoven measurements at the oven S100, determining a set of foodstufffeatures based on the oven measurements S200, determining cookinginstructions based on the foodstuff features S300, and dynamicallycontrolling oven operation based on the cooking instructions S400. Themethod can be performed by the system disclosed above (e.g., the oven,the processor of the oven, etc.), or be performed with any othersuitable system. All or a portion of the method can be performed by theremote computing system, oven (e.g., as disclosed above, alternatively adifferent oven), user device, and/or any other suitable computingsystem. All or a portion of the method can be performed in real- ornear-real time, after a delay, concurrently, asynchronously, or with anyother suitable temporal relationship. All or a portion of the method canbe performed automatically, pseudo-manually (e.g., in response toreceipt of a user input), or at any other suitable time. All or aportion of the method can be repeated, performed periodically, orperformed at any other suitable frequency. Multiple method instances fordifferent ovens, foodstuffs, or users can be performed in parallel, inseries, or with any other suitable temporal relationship. However, themethod can be otherwise performed.

The method can function to automatically adjust oven operation during acooking session to achieve a target food parameter (e.g., a targetcrispness, etc.) with little or no manual input. Furthermore, becauseoven operation is automatically controlled, different operationparameters, such as temperature gradients and airflow, can be variedand/or more precisely controlled over the course of the cooking session.For example, instead of baking prime rib at a substantially constant lowtemperature, the oven enables the prime rib to be cycled through highand low temperatures throughout its cooking period. In another example,the oven can dynamically determine that there is more foodstuff mass tothe right of the cavity than the left (e.g., based on the weightsensors), and increase heat output from the right heating elementsrelative to the left heating elements. The method can additionallyfunction to automatically identify the foodstuff within the cavity basedon the sensor measurements (e.g., image, weight, temperature, etc.), andautomatically suggest cooking instructions for, or automatically cookthe foodstuff, based on the foodstuff identity. The method canadditionally function to stream images of the foodstuff (e.g., offoodstuff cooking in-situ) to the user device or other endpoint duringthe cooking process. The method can additionally function to continuallymonitor all instances of foodstuff cooking in-situ, for one or moreovens. The method can additionally function to generate a set ofsupervised training data through typical oven use, which can be used totrain modules to automatically identify foodstuff. The method canadditionally function to automatically generate recipes through the actof cooking (e.g., via user remote control), or perform any othersuitable functionality.

The method can be performed in association with a set of targetparameters for the foodstuff. The target parameters can be targetfoodstuff parameters, oven operation parameters, or any other suitableparameter. The target parameters can be for a given stage in the cookingprocess (e.g., minutes after foodstuff introduction into the cavity), atarget endpoint parameter (e.g., target cooking parameter value when thefood is “done,” removed from the oven, and/or ready for consumption), orbe any other suitable target parameter. Examples of target parametersinclude the amount of bread browning, amount of skin or surfacecrispness, water content of the foodstuff, water ratio of the foodstuff,denatured protein uniformity throughout the foodstuff, volume of apredetermined sound frequency (e.g., corresponding to a cooking stage),duration that a predetermined sound frequency has been substantiallymaintained, a weight loss ratio, and a threshold concentration of agiven volatile or aromatic compound being released (e.g., 5 g/L, mol/L,etc.). However, the target parameter can be any other suitable targetparameter.

The target parameters can be preferences of a user (e.g., received fromthe user or determined from a history of user oven use), wherein theuser can be a primary user, user proximal the oven during the foodstuffinsertion event (e.g., connected to the oven via a short-rangecommunications channel, determined from a user image, etc.), or anyother user; preferences of a user population (e.g., average parametervalue, etc.); specified by an entity (e.g., a chef); recommendedparameters (e.g., from food safety agencies, etc.); or be determined inany other suitable manner. The target parameters can be received from auser (e.g., at the oven, at a user device), retrieved based on afoodstuff identifier (e.g., bar code, etc.), read from the foodstuffidentifier (e.g., QR code, etc.), dynamically determined by the oven orremote computing system (e.g., based on the foodstuff parameters, suchas foodstuff type, weight, starting temperature, ambient humidity,etc.), specified in a recipe, or otherwise determined.

The recipes can be stored in a database, wherein the database can bestored by the remote system, oven, user device, or any other suitablecomputing system. Each recipe can specify a temperature schedule for thecavity or foodstuff, an airflow schedule (e.g., mass flow, velocity,etc.), the amount of power to apply to the heating element and/orconvection element at any given time, or specify any other suitableparameter value for a cooking session. The stored recipes and/or targetparameters can be changed or updated based on: new user input, newdemographic preferences, new identified users, or based on any othersuitable data. In one example, the user can select a new targetfoodstuff parameter (e.g., “browner”) for the foodstuff currentlycooking in-situ, the system can automatically determine a set of targetoven operation parameters to achieve the target foodstuff parameter(e.g., increase power the top heating elements, increase fan speed), andthe oven can be automatically controlled to meet the set of target ovenoperation parameters. The set of target oven operation parameters can bedetermined based on the cooking history for the in-situ foodstuff, basedon a cooking response profile for the foodstuff (e.g., determined fromthe cooking history and/or response for similar foodstuff from otherovens), or otherwise determined.

The method can additionally be used with a set of identification modulesthat function to automatically identify the foodstuff in-situ. Theidentification module can classify the foodstuff (e.g., function as aclassification module), determine a value for each of a set of foodstuffparameters, such as crispness or doneness (e.g., function as aregression module), extract foodstuff parameters from the sensormeasurements for subsequent analysis, or perform any other suitablefunctionality. Each module of the set can utilize one or more of:supervised learning (e.g., using logistic regression, using backpropagation neural networks, using random forests, decision trees,etc.), unsupervised learning (e.g., using an Apriori algorithm, usingK-means clustering), semi-supervised learning, reinforcement learning(e.g., using a Q-learning algorithm, using temporal differencelearning), and any other suitable learning style. Examples ofreinforcement learning include using a brute force approach, valuefunction approach (e.g., Monte Carlo method, temporal differencemethod), direct policy approach, or otherwise reinforced. Examples ofsupervised learning include using: analytical learning, artificialneural network, backpropagation, boosting (meta-algorithm), Bayesianstatistics, case-based reasoning, decision tree learning, inductivelogic programming, Gaussian process regression, group method of datahandling, kernel estimators, learning automata, minimum message length(decision trees, decision graphs, etc.), multilinear subspace learning,Naive Bayes classifier, nearest neighbor algorithm, probablyapproximately correct learning (pac) learning, ripple down rules, aknowledge acquisition methodology, symbolic machine learning algorithms,subsymbolic machine learning algorithms, support vector machines,minimum complexity machines (mcm), random forests, ensembles ofclassifiers, ordinal classification, data pre-processing, handlingimbalanced datasets, statistical relational learning, or otherwiselearned. Examples of unsupervised learning include using a method ofmoments approach or otherwise learned.

Each module of the set can implement any one or more of: a regressionalgorithm (e.g., ordinary least squares, logistic regression, stepwiseregression, multivariate adaptive regression splines, locally estimatedscatterplot smoothing, etc.), an instance-based method (e.g., k-nearestneighbor, learning vector quantization, self-organizing map, etc.), aregularization method (e.g., ridge regression, least absolute shrinkageand selection operator, elastic net, etc.), a decision tree learningmethod (e.g., classification and regression tree, iterative dichotomiser3, C4.5, chi-squared automatic interaction detection, decision stump,random forest, multivariate adaptive regression splines, gradientboosting machines, etc.), a Bayesian method (e.g., naive Bayes, averagedone-dependence estimators, Bayesian belief network, etc.), a kernelmethod (e.g., a support vector machine, a radial basis function, alinear discriminate analysis, etc.), a clustering method (e.g., k-meansclustering, expectation maximization, etc.), an associated rule learningalgorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.), anartificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), a deeplearning algorithm (e.g., a restricted Boltzmann machine, a deep beliefnetwork method, a convolution neural network method, a stackedauto-encoder method, etc.), a dimensionality reduction method (e.g.,principal component analysis, partial lest squares regression, Sammonmapping, multidimensional scaling, projection pursuit, etc.), anensemble method (e.g., boosting, bootstrapped aggregation, AdaBoost,stacked generalization, gradient boosting machine method, random forestmethod, etc.), and any suitable form of machine learning algorithm. Eachmodule of the set can additionally or alternatively be a: probabilisticmodule, heuristic module, deterministic module, or be any other suitablemodule leveraging any other suitable computation method, machinelearning method, or combination thereof.

All or a subset of the modules can be validated, verified, reinforced,calibrated, or otherwise updated based on newly received, up-to-datemeasurements of the cooking session; past foodstuff measurementsrecorded during cooking sessions; historic foodstuff measurementsrecorded during past cooking sessions, or be updated based on any othersuitable data. All or a subset of the modules can be run or updated:once; every year; every time the method is performed; every time anunanticipated measurement value is received; or at any other suitablefrequency. All or a subset of the modules can be run or updatedconcurrently, serially, at varying frequencies, or at any other suitabletime. All or a subset of the modules can be validated, verified,reinforced, calibrated, or otherwise updated based on newly received,up-to-date data; past data or be updated based on any other suitabledata. All or a subset of the modules can be run or updated: in responseto receipt of a new foodstuff classification by a user (e.g., new userconfirmation), in response to determination of a discrepancy between themodule output(s) and the actual foodstuff class, or at any othersuitable frequency.

2.1 Recording Oven Measurements.

Recording the set of oven measurements S100 functions to generate a setof raw data. The raw data can be used to: classify foodstuff, monitorfoodstuff cooking status, generate training sets (e.g., supervised orunsupervised), or perform any other suitable downstream process. The setof oven measurements can be recorded by the sensors of the oven, by auser device associated with the oven (e.g., a mobile device electricallyconnected or wirelessly connected to the oven), by an auxiliary device(e.g., a temperature probe), by a secondary connected device (e.g., aconnected weight, a connected refrigerator, a connected sous videmachine, etc.), or be measured by any other suitable component. Thesensors used to record the measurements can be preferably non-contactsensors, contact sensors (e.g., probes), or any other suitable sensor.

Oven measurements (sensor measurements) can be measured for a singletimestamp, or across a time period. Oven measurements can be recorded ata predetermined frequency (e.g., during the cooking session), inresponse to the occurrence of a recording event (e.g., in response tooccurrence of a food insertion event, in response to receipt of a queryfrom a user device, in response to expiration of a cooking timer, etc.),or be recorded at any other suitable time.

Oven measurements can include foodstuff measurements, oven measurements,user measurements, or any other suitable set of measurements. Ovenmeasurements can include values, patterns over time, associationsbetween a first and second sensor measurement (e.g., a ratio, etc.), orany other suitable characterization. Examples of foodstuff measurementsinclude color images (e.g., stream, such as a video, or single images),thermal images, surface temperature (estimated or actual), internaltemperature (estimated or actual), audio, humidity (e.g., internal orexternal), weight, weight distribution, volatile molecule composition,or any other suitable food parameter. Examples of oven measurements oroven parameters include: target temperatures, cavity temperatures (e.g.,average, at given point in the cavity, gradients across the cavity,etc.), airflow (volume, rate, direction, etc.), applied light,electromagnetic emission within the cavity, the heating elementidentifier (e.g., which heating element is being controlled), the fanidentifier (e.g., which fan is being controlled), the sensor identifier,or any other suitable parameter of oven operation.

The oven measurements can be used by the oven itself, sent to the remotesystem, sent to the user device, or otherwise managed. The ovenmeasurements can be sent to the remote system by the oven, by a userdevice associated with the oven (e.g., a mobile device electricallyconnected or wirelessly connected to the oven), by an auxiliary device(e.g., a temperature probe), by a secondary connected device (e.g., aconnected weight, a connected refrigerator, a connected sous videmachine, etc.), or be sent by any other suitable component. However, theoven measurements can be otherwise sent to the remote system.

The method can additionally include illuminating the cooking cavity(e.g., with the set of light emitting elements), which functions toilluminate the cooking cavity and foodstuff for subsequent in-personviewing, image capture (e.g., streaming, food identification, etc.), andother visual applications. The cooking cavity can be illuminated inresponse to detection of the foodstuff insertion event, during thecooking session (e.g., continuously or periodically), in response to theuser turning on the light, or at any other suitable time. In onevariation, illuminating the cooking cavity includes operating the lightemitting elements to meet a predetermined set of illumination parameters(e.g., intensity, color, etc.).

In a second variation, illuminating the cooking cavity includesoperating the light emitting elements to meet a target set ofillumination parameters. This embodiment can function to accommodate forambient light leaking through transparent portions of the oven. In oneembodiment, light emitting element operation is incrementally adjusteduntil a target illumination parameter is met (e.g., as determined by alight sensor within the cavity, by the camera, etc.). In a secondembodiment, parameters of the ambient light are determined (e.g., by theoven processor, based on the ambient light sensor), illuminationparameters for the light emitting elements are determined based on theambient light parameters (e.g., cooperatively achieve a set of targetillumination parameters with the ambient light), and the light emittingelements are controlled to meet the illumination parameters. However,the cavity can be otherwise illuminated. The target set of illuminationparameters can be predetermined, selected for streaming, for imageanalysis, for in-person viewing (e.g., to minimize blinding a user atnight), or otherwise selected. In a specific example, the light emittingelements can be operated to meet a first set of target illuminationparameters to capture an initial image for foodstuff identification, andsubsequently operated to meet a second set of target illuminationparameters tailored toward streaming video of the food in-situ. However,the cooking cavity and/or foodstuff can be otherwise illuminated.

In one example, the oven can additionally stream a video of the ovencavity and/or cooking food products to the remote system. In a secondexample, the oven can record and/or send sensor measurements to theremote system: periodically, in response to the occurrence of a triggerevent (e.g., food insertion event, send event, etc.), or at any othersuitable time. Measurements from different sensors can be concurrentlyrecorded or asynchronously recorded (e.g., within a predetermined timeduration, separated by a predetermined time duration, etc.), orotherwise recorded. However, the oven measurements can be otherwiserecorded.

2.2 Determining Foodstuff Features.

Determining foodstuff features based on the oven measurements S200functions to determine a characterization of the foodstuff within thecavity. The foodstuff features can be used to identify the foodstuff,monitor foodstuff cooking progress, or be used in any other suitablemanner. The foodstuff features are preferably determined by the oven,but can alternatively be entirely or partially determined by the remotecomputing system. Foodstuff features can include: image features (e.g.,foodstuff features extracted from an image of the foodstuff), weight,temperature (internal, external, etc.), humidity, density, or any othersuitable feature. The foodstuff features can be extracted from the ovenmeasurements (e.g., from the foodstuff image), be the oven measurementsitself, or be otherwise determined.

Foodstuff features can be determined in response to occurrence (e.g.,detection) of a foodstuff insertion event. The foodstuff insertion eventcan be: an oven door opening event (e.g., the door latch unlatching), anoven door closing event, an oven weight increase, recordation of aninertial measurement pattern indicative of door opening, a combinationof the above, or recordation any other suitable set of measurements.Foodstuff features can additionally or alternatively be determined whilethe foodstuff is being cooked by the oven. For example, the foodstufffeatures can be determined: periodically, in response to occurrence of apredetermined event, in response to recordation of a predetermined setof oven measurements.

In a first variation, the foodstuff features include foodstuff weight.In a first embodiment, the foodstuff weight can be determined from theload sensors (e.g., weight sensors) of the oven. This can include:recording load measurements from one or more load sensors, determining achange in the load measurement values, and determining the foodstuffweight as the difference. In a second embodiment, the foodstuff weightcan be received from a user device. In a third embodiment, the foodstuffweight can be received from a connected scale. However, the foodstuffweight can be otherwise determined.

In a second variation, the foodstuff features include weightdistribution. In a first embodiment, determining foodstuff weightdistribution includes comparing the load measurements from a first,second, third, and fourth load sensor arranged along a first, second,third, and fourth corner of the oven, and locating the foodstuff masswithin the cooking cavity based on the differences between the loadmeasurements. However, the foodstuff distribution can be otherwisedetermined.

In a third variation, the foodstuff features can be extracted from animage of the foodstuff. Foodstuff features that can be extracted fromthe image include: foodstuff shape, size (e.g., physical size), color(e.g., average color, color gradients, etc.), contour, texture, number(e.g., count), spatial distribution (e.g., foodstuff mass to the rightor left of the cooking cavity), or any other suitable feature that canbe visually determined. Foodstuff can additionally be recognized fromthe image, using convolutional neural network methods, appearance-basedmethods (e.g., edge matching, divide-and-conquer searching, grayscalematching, gradient matching, histograms, large modelbases, etc.),feature-based methods (e.g., interpretation trees, hypothesizing, poseconsistency, pose clustering, invariance, geometric hashing,scale-invariant feature transform, speeded up robust features, etc.),generic algorithms, or using any other suitable method. In onevariation, foodstuff features are determined from the image using:per-pixel classification, pixel grouping and/or clustering (e.g., toidentify like pixels), and/or cluster classification (e.g., to determinewhether a group of pixels is representative of a rack, tray, foodstuff,etc.). However, the foodstuff features can be otherwise determined.

The image can be the raw image recorded by the camera or bepre-processed. Pre-processing the image can include: dewarping the image(e.g., dewarping a distorted image, such as a fisheye image), mosaicingmultiple images together, resizing the image, filtering the image,removing images of the ambient environment captured in the image (e.g.,cropping the image, transforming the image using a transfer function,etc.), or otherwise pre-processing the image. In a specific example,processing the raw image can include: identifying a set of input pixels,mapping each input pixel (e.g., of an input set) to an output pixel(e.g., of an output set) based on a map, and interpolating the pixelsbetween the resultant output pixels to generate an output frame. Theinput pixels can optionally be transformed (e.g., filtered, etc.) beforeor after mapping to the output pixel. The map can be determined based onprocessing instructions (e.g., predetermined, dynamically determined),or otherwise determined. The input pixels can be a subset of the imageframe (e.g., a segment of the frame, wherein the input pixels arecontiguous; a sampling of the frame, wherein the input pixels areseparated by one or more intervening pixels), be the entirety of theimage frame, or be any other suitable portion of the frame. One or moreinput pixel sets can be identified from the same frame, and processedinto different output views (e.g., a “birdseye view” of the cookingcavity, a “zoomed in” view of the foodstuff on the right, a “zoomed in”view of the foodstuff on the left, etc.); alternatively, a single inputpixel set can be identified from any given frame. The image can bepre-processed by the oven (e.g., by multi-purpose processing units,dedicated processing units, etc.), by the remote computing system, bythe user device, or by any other suitable computing system.

In one variation, extracting foodstuff features from the image S210 caninclude: segmenting the image into a foreground and a background S211,and extracting foodstuff features from the foreground S213. In someembodiments, image segmentation into the foreground and background canbe streamlined by providing a known, constant-color background (e.g.,black, white, etc.). The background can be the baking tray, cookingcavity interior, or any other suitable portion of the oven opposing thecamera. The background can additionally be matte (e.g., low-reflectance)to facilitate rapid segmentation. In one embodiment, the foreground caninclude the foodstuff, the tray, and the oven rack. In a secondembodiment, the foreground can include the foodstuff and the oven rack(e.g., wherein the tray and background are the same color). In a thirdembodiment, the foreground can include the foodstuff only. However, theforeground can be otherwise defined. The image can be segmented using:thresholding, clustering methods, compression-based methods,histogram-based methods, edge detection, dual clustering method,region-growing methods, partial differential equation-based methods(e.g., parametric methods, level set methods, fast marching methods,variational methods, graph partitioning methods (e.g., Markov randomfields; supervised image segmentation using MRF and MAP; optimizationalgorithms, such as iterated conditional modes/gradient descent,simulated annealing, etc; unsupervised image segmentation using MRF andexpectation maximization, etc.), watershed transformation, model basedsegmentation, multi-scale segmentation (e.g., one-dimensionalhierarchical signal segmentation, image segmentation and primal sketch),semi-automatic segmentation, trainable segmentation, or any othersuitable segmentation method.

In one embodiment, extracting foodstuff features from the foregroundincludes determining a physical foodstuff shape (e.g., foodstuffboundary). This can include identifying a continuous region indicativeof foodstuff (e.g., contiguous region indicative of foodstuff) withinthe foreground and determining the perimeter of the contiguous region(e.g., using edge detection techniques, gradient descent techniques,etc.).

In a second embodiment, extracting foodstuff features from theforeground includes determining a physical foodstuff area (e.g.,foodstuff projection). This can include: identifying a contiguous regionindicative of foodstuff within the foreground, and determining thefoodstuff area based on the amount of the image occupied by thecontiguous region. However, the foodstuff area can be otherwisedetermined from the foreground. In a first example, the foodstuff areacan be determined based on the estimated physical area captured withinthe image and the percentage of the image occupied by the identifiedregion. In a second example, the foodstuff area can be determined basedon the number of contiguous pixels within the identified region and apixel-to-physical area map. The foodstuff area can be the number ofpixels within the region multiplied by the pixel-to-physical area ratio,or be otherwise determined.

In a third embodiment, extracting foodstuff features from the foregroundincludes determining a physical foodstuff height. Determining thefoodstuff height can include determining the foodstuff top distance fromthe oven top (e.g., determining foodstuff proximity to the oven top orcamera), or determining any other suitable foodstuff parameter along thez-axis. The foodstuff height can be determined based on the rack levelused (e.g., based on load sensors within the rack support, based onadjacent wire distance within the image, based on total tray-to-wallgap, etc.), the foodstuff contour, or based on any other suitableparameter.

In a first example, the foodstuff height can be determined based on thefoodstuff classification (e.g., wherein the food class is typicallyflat, round, etc.). In a second example, the foodstuff height isdetermined based on the foodstuff contour. The foodstuff contour can bedetermined using a scanner (e.g., 3D scanner), using a plurality ofimages recorded under different conditions, or otherwise determined. Ina first specific example, the contour can be determined from a first andsecond image recorded from a first and second visual angle (e.g., from astereoscopic camera, from a single camera that has been moved, etc.). Ina second specific example, the contour can be determined from a firstand second image using different illumination angles, wherein thecontour can be determined based on shadow analysis. For example, a firstand second light emitting element, arranged along a first and secondside of a camera, can be selectively turned on and off, wherein thecamera captures the first image when first light emitting element is onand second is off, and captures second image when first light emittingelement is off and second is on. However, the contour can be otherwisedetermined.

Extracting the foodstuff features from the image S210 can optionallyinclude determining a pixel-to-physical area map for the image S215.This can function to accommodate for perspective differences due tofoodstuff arrangement on different rack levels. This can be performedprior to foodstuff feature determination from the image, or be performedat any other suitable time. Different pixel-to-physical area maps arepreferably associated with different rack levels, but different racklevels can alternatively share a pixel-to-physical area map. Thepixel-to-physical area maps for each rack level is preferablypredetermined, but can alternatively be dynamically determined orotherwise determined.

In a first variation, determining the pixel-to-physical area map caninclude identifying the rack level being used. In one example, the racklevel used can be determined based on load cells arranged within therack supports.

In a second variation, the pixel-to-physical area map can be determinedbased on the rack image, wherein the rack has a known distance betweenadjacent wires (e.g., wherein the adjacent wires appear further apartwhen the rack is on the top level, and appear closer together when therack is on the lower level, such that the apparent adjacent wiredistance functions as a reference). This variation can include:identifying adjacent wires of an oven rack within the image; determininga pixel distance between the adjacent wires within the foreground; anddetermining pixel-to-physical area mapping based on the pixel distance(e.g., selecting the map associated with the determined pixel distance).

In a third variation, the pixel-to-physical area map can be determinedbased on the gap between the oven wall and baking tray edges, whereinthe baking tray has known dimensions (e.g., wherein the gap can functionas a reference). This variation can include: identifying the baking trayperimeter within the image; determining a total pixel distance betweenthe baking tray edges and the oven walls; and determiningpixel-to-physical area mapping based on the total pixel distance (e.g.,selecting the map associated with the determined pixel distance).

In a fourth variation, the pixel-to-physical area map can be determinedbased on the number of rack supports (e.g., tracks, levels, etc.)visible within the image. For example, a first pixel-to-physical areamap associated with a lowermost rack position can be selected ifportions of two rack supports are visible within the image; a secondpixel-to-physical area map associated with a middle rack position can beselected if portion one rack support is visible within the image; and athird pixel-to-physical area map associated with an uppermost rackposition can be selected if no rack supports are visible within theimage. However, the pixel-to-physical area map can be otherwisedetermined.

In a fifth variation, the pixel-to-physical area map can be determinedbased on the rack position specified by a user (e.g., wherein a userselects the rack position). In one embodiment of this variation, themethod can additionally include: in response to food insertion into thecavity and initial foodstuff image recordation; estimating a rackposition based on the initial foodstuff image; presenting an identifierfor the estimated rack position to the user; in response to userconfirmation of the estimated rack position, using the pixel-to-physicalarea map associated with the estimated rack position; and in response touser selection of a different rack position, using the pixel-to-physicalarea map associated with the user-selected rack position. The estimatedrack position, initial foodstuff image, and/or user selection (orconfirmation) can additionally be used to train a rack positiondetermination module (e.g., using machine learning techniques) by theoven, remote computing system, user device, or other system. In a secondembodiment, the method can include suggesting a rack level (rackposition) based on the identified foodstuff (e.g., based on anassociated recipe, etc.). This embodiment can optionally includetracking whether a user takes subsequent action (e.g., opens the door,lifts the tray off the rack level, re-inserts the tray, etc.), whereinthe subsequent user action can be used to train the rack positiondetermination module.

However, the pixel-to-physical area map can be determined in any othersuitable manner. However, the foodstuff size can be otherwisedetermined.

Determining foodstuff features can additionally include identifying thefoodstuff within the cooking cavity S230, which can subsequently be usedto determine the cooking instructions. The foodstuff can be identifiedin response to foodstuff insertion into the oven, in response to receiptof a user input, or at any other suitable time. The foodstuff ispreferably identified once per cooking session, but can alternatively beidentified any suitable number of times per cooking session. Thefoodstuff is preferably identified by the oven, but can alternatively beidentified by the remote computing system, user device, user, or anyother suitable system. The foodstuff can be identified based onfoodstuff parameters, such as weight, temperature, shape, color,variations of the aforementioned parameters over space, variations ofthe aforementioned parameters over time (e.g., velocity of change,acceleration of change, etc.), or based on any other suitable foodstuffparameters. Additionally or alternatively, the foodstuff can beidentified based on an identifier, such as a color, a QR code, abarcode, a user selection, or any other suitable identifier. Theidentifier can be extracted from the images recorded by the oven,identified by a code reader on the oven, identified by a code reader onthe user device, received from the user interface unit, or identified inany other suitable manner.

In a first variation, the foodstuff is identified based on a foodstuffidentifier determined from the foodstuff packaging (e.g., scanned, read,etc.). The foodstuff identifier can be determined from a QR code,barcode, or any other suitable identifier associated with the foodstuff.The foodstuff identifier can be read by the in-chamber camera, by acamera on the oven door, by the user device, by a BLE radio, or by anyother suitable system.

In a second variation, the foodstuff is identified (e.g., the foodstuffclass determined) based on foodstuff features using an identificationmodule (e.g., a set of classification modules) S231. Foodstuff featuresused can include image features (e.g., size, shape, color, area,boundaries, saturation, gradients, etc.), weight, temperature, or anyother suitable foodstuff feature. The oven, remote computing system,user device, or other computing system can classify the foodstuff, usingan identification module stored locally. Copies of the classificationsystem can be stored by the other connected computing systems. Theremote computing system is preferably the source of truth (e.g., whereinthe classification system stored by the remote computing system hashigher priority than other copies), but any other suitable system canfunction as the source of truth. When the foodstuff is classified by anon-oven system, the non-oven system preferably receives the image,classifies the foodstuff within the image, and sends the classificationor information associated with the classification (e.g., a recipe) backto the oven. However, the foodstuff can be otherwise classified.

In one embodiment of the second variation, the system can include adifferent identification module for each food class, wherein theclassification system determines a probability for each of a pluralityof food classes. The different identification modules can use differentsubsets of foodstuff features, or use the same set. In a secondembodiment, the system can include a single identification module forall food classes, wherein the classification system outputs a singlefood class. This identification module can utilize a set of chainedmodules, each considering a different subset of foodstuff features, thatprogressively narrow down the possible food classes. However, theidentification module can consider any suitable set of food features,and be structured in any other suitable configuration.

The second variation can additionally include receiving a userclassification for the foodstuff class S233, which functions to generatea supervised training set for subsequent classification systemrefinement. Receiving a user classification of the foodstuff class caninclude: displaying an identifier for the foodstuff class in response tofoodstuff identification at a user interface (e.g., of the oven, userdevice, etc.); and receiving a user input based on the displayedidentifier (e.g., at the user interface, at a second user interface,etc.). The foodstuff class identifier can be text (e.g., a name), animage (e.g., a generic image, a historic image from a past cookingsession, etc.), or any other suitable identifier. In one embodiment, asingle identifier is displayed on the user interface for the foodstuffcurrently within the cavity. In a second embodiment, a list ofidentifiers for potential foodstuff classes is displayed on the userinterface for the foodstuff currently within the cavity. The foodstuffclass identifiers are preferably presented in decreasing probabilityorder, but can alternatively be presented in increasing probabilityorder, randomized, in decreasing popularity order (e.g., for apopulation or the user), or presented in any other suitable order.Receiving the user classification can include: receiving a userselection of an identifier for a foodstuff class at the user interface(e.g., from the list), receiving a user confirmation or denial of thesingle presented identifier, receiving a cooking initiation selectionfrom the user, or receiving any other suitable user input.

Receiving user confirmation of the foodstuff class S233 can additionallyinclude sending the user classification and the data associated with theoven measurements to the remote computing system. The data associatedwith the oven measurements can be the oven measurements from which thefood classification was determined, the foodstuff features from whichthe food classification was determined, or include any other suitabledata. The user classification and data can be sent through the userdevice, sent directly to the remote computing system, or otherwisetransmitted from the oven.

Receiving user confirmation of the foodstuff class S233 can additionallyinclude updating the identification module(s). The identificationmodules can be updated by the oven, by the remote computing system, bythe user device, or by any other suitable system. The identificationmodules can be updated based on the user classification, the dataassociated with the user classification (e.g., underlying images, sensormeasurements, etc.), parameters of the user account associated with theuser classification (e.g., classification reliability, demographic,etc.), parameters of the oven from which the user classification wasreceived (e.g., sensor calibration, sensor measurement reliability,etc.), historic user classifications, and/or any other suitableinformation. The entire identification module can be updated, specificsub-modules can be updated (e.g., the identification module for thefoodstuff class), or any suitable set of sub-modules can be updated. Theidentification modules can be updated for a single oven, for a subset ofovens (e.g., ovens sharing a similar parameter, such as ambientparameters), for all ovens, for a single user, for a subset of users(e.g., users sharing a similar parameter, such as demographic,location), for all users, or for any other suitable subset of the ovenor user population.

The identification modules can be updated in response to the userclassification differing from the foodstuff parameters identified by theidentification modules (e.g., foodstuff class, foodstuff feature value,etc.) beyond a threshold difference, periodically, or be updated at anyother suitable frequency. In one example, the identification module isupdated in response to the identified class differing from the userclassification. In a second example, the identification module isupdated in response to the identified feature value differing from theuser classification beyond a threshold value difference. However, theidentification module can be updated at any other suitable time.

The identification modules can be updated during the cooking session, inresponse to receipt of the user confirmation (e.g., in near-real time),updated after the cooking session, updated periodically based onmultiple user confirmations (e.g., from one or more users), updated inresponse to a threshold number of user confirmation receipt, ordetermined at any other suitable time. In one example, different ovenscan be concurrently trained on different user classification sets,wherein the resultant identification modules can be subsequentlycompared (e.g., periodically, in response to a threshold condition beingmet, etc.) to identify the more accurate and/or precise model. Theidentified model can subsequently be used as the global classificationmodel (e.g., sent to all ovens), or otherwise used. When theidentification module is updated by computing system separate anddistinct from the oven, the updated identification module can be sent(e.g., pushed) to the oven, wherein the oven can store and use theupdated identification module to identify foodstuff (e.g., recognizefoodstuff) for subsequent cooking sessions. The updated identificationmodule can be sent to the oven in near-real time (e.g., uponidentification module updating), periodically, at a predetermined timeof the day (e.g., 3 am), when the user is absent from the house (e.g.,as determined from auxiliary connected systems or otherwise determined),or at any other suitable time.

In one variation, updating the identification module can include:retrieving a copy of the identification module; in response to the userclassification substantially matching the foodstuff class identified bythe identification module (e.g., the foodstuff class having the highestprobability or probabilities), determining a second identificationmodule by reinforcing the copy of the identification module based on thedata and the user classification; in response to the selected identifierdiffering from the foodstuff class identified by the identificationmodule, determining a second identification module by re-training thecopy of the identification module based on the data and the userclassification; and automatically updating the oven with the secondidentification module. In this variation, the identification module ispreferably calibrated by the remote computing system, but canalternatively be calibrated by any other suitable computing system.Retrieving a copy of the identification module can include: retrievingthe copy from remote computing system storage (e.g., retrieving a globalcopy, retrieving a copy associated with the oven, retrieving a copyassociated with the user account, retrieving a identification moduleversion identified by the oven, etc.); requesting and/or receiving thecopy from an oven (e.g., the oven from which the user classification wasreceived, a second oven, etc.), or retrieving the copy in any othersuitable manner. However, the identification module can be otherwiseupdated.

2.3 Determining Cooking Instructions Based on the Foodstuff Features.

Determining cooking instructions based on the foodstuff features S300functions to determine future cooking instructions for the foodstuffcurrently within the oven, or record the cooking parameters of thecurrent cooking session (based on the foodstuff currently within theoven) for future use in other cooking sessions. The cooking instructionsare preferably determined by the remote system, but can alternatively bedetermined by the user device, the oven, a secondary user's device, orany other suitable computing system. The cooking instructions arepreferably determined in response to receipt of the user confirmation(e.g., receipt of the identification selection), but can alternativelybe determined in response to foodstuff detection within the cookingcavity, be determined in anticipation of foodstuff insertion into thecavity (e.g., based on a secondary cooking apparatus, a mealplan, etc.),or be determined at any other suitable time. The cooking instructionsare preferably specific to the foodstuff class, and can additionally oralternatively be specific to the user associated with the cookingsession, the oven, a set of users (e.g., sharing a common parameter), orotherwise specified. Alternatively, the cooking instructions can begeneric. The user associated with the cooking session can be identifiedthrough: the identifier for the user device connected to oven temporallyproximal the foodstuff insertion event, biometrics (e.g., received fromoven user input during foodstuff insertion, selection entry, etc.), orotherwise determined.

The cooking instructions can be oven operation instructions, new targetfoodstuff parameter values, user oven interaction instructions (e.g.,displayed on an oven display or on the user device), or be any othersuitable instruction. Oven operation instructions (e.g., recipes) caninclude target oven parameter values (e.g., wherein the ovenautomatically converts the target values into control instructions),component control instructions, oven parameter value patterns (e.g.,target oven parameter values associated with predetermined timestamps),oven parameter values associated with foodstuff cooking states, ovenoperation states (e.g., on, off, standby, etc.), or any other suitableoperation instruction. Examples of oven operation instructions include:target oven parameter values, such as a target cavity temperature;control instructions for a set of heating elements 300, such as theamount of power to supply to each individual heating element; controlinstructions for a set of fans, such as the rotation speed for eachindividual fan; or be any other suitable oven operation instruction.Examples of user interaction instructions include instructing the userto open the oven door (e.g., when airflow provided by the fans isinsufficient, such as when making macarons). However, any other suitablecooking instruction can be generated.

In a first variation, the cooking instructions (e.g., recipe) areretrieved based on a foodstuff identifier, but can be otherwisedetermined (e.g., from a mealplan, etc.). The foodstuff identifier canbe scanned from the food or food packaging in-situ, automaticallydetermined based on the sensor measurements (e.g., from the foodstuffimage), received from a user (e.g., the selected identifier), orotherwise determined. The cooking instructions can be retrieved from theoven's on-board storage (e.g., wherein the oven stores recipes for eachof a plurality of foodstuff classes), from a remote computing system, orfrom any other suitable endpoint. The cooking instructions can beretrieved by the system identifying the foodstuff, the oven, or anyother suitable system. When the system identifying the foodstuff is notthe oven, the system can additionally send the cooking instructions tothe oven. For example, the system can automatically determine that thefood within the oven is chicken (e.g., based on image analysis ormachine vision), determine that the chicken is frozen, and automaticallyselect a cooking pattern (e.g., baking temperature pattern) to thaw andcook the chicken to the user's preferences. However, the cookinginstructions can be otherwise determined.

This variation can optionally include adjusting the retrieved cookinginstructions based on the sensor measurements (e.g., foodstuffparameters, oven parameters), wherein the cooking elements arecontrolled based on the adjusted cooking instructions. For example, aninitial warmup period can be extended or added in response todetermination that the foodstuff is frozen or that the cooking cavityhas not been preheated. In a second example, the cooking instructionscan be dynamically adjusted during the cooking session to accommodatefor foodstuff performance. In a specific example, the method can includedetermining a cooking status based on the foodstuff parameters (e.g.,monitoring the foodstuff while cooking), comparing the actual cookingstatus and expected cooking status, and adjusting the cookinginstructions based on the comparison (e.g., to meet a target cookingstatus by a predetermined time). However, the instructions can beotherwise adjusted.

In a second variation, as shown in FIG. 26, the method includesrecording the current cooking parameters S101, which can function tomonitor the current cooking state of the food and/or automaticallygenerate a recipe from the user's cooking session, such that the cookingparameters function as the cooking instructions for a future cookingsession. In one embodiment, the method records the oven operatingparameters during the cooking session. In a second embodiment, themethod records the foodstuff parameters during the cooking session,wherein the foodstuff parameters can be directly measured or determinedfrom a set of foodstuff measurements taken by the oven or secondarysensor. For example, the remote system can analyze the audio and/orimages streamed from the oven to categorize the cooking stage of thefood, the color profile of the food, or any other suitable foodstuffparameter. The categorization can be learned (e.g., based on a trainedset, dynamically learned from user feedback on the images and/or audio,etc.), assigned by the user, or otherwise determined.

Recording the current cooking parameters S310 can include recording thecurrent cooking parameters by the oven, sending the recorded cookingparameters to a remote system, and storing the current cookingparameters in association with a user account at the remote system. Theuser account can be manually generated by the user, automaticallygenerated (e.g., based on the preferences of a user's social network,extracted from the user's social networking system accounts, based onhistoric cooking sessions, etc.), or otherwise generated. The cookingparameters can additionally be associated with a recipe (e.g., authoredby the user or authored by a secondary user, wherein the recordedcooking parameters can be used to fine-tune the secondary user's recipefor other users or for the primary user), a user preference (e.g.,wherein the cooking parameters can be analyzed to extract a cookingpreference for the user), or associated with any other suitableinformation.

In a third variation, as shown in FIG. 25, the method includesdetermining future cooking parameter values. Determining future cookingparameter values can include recording the current cooking parametervalues (e.g., as in the first variation), estimating a future cookingparameter value based on the current cooking parameter values, comparingthe estimated cooking parameter value to a target cooking parametervalue, and generating instructions that rectify differences between theestimated cooking parameter value and the target cooking parametervalue. However, the future cooking parameter values can be otherwisedetermined.

In one example of the second variation, the method includes determiningthe current cooking state of the food (current foodstuff parametervalues) within the oven cavity, estimating the future cooking state ofthe foodstuff at a future time based on the current cooking state and/orthe current oven operation parameters, comparing the estimated cookingstate to a target cooking state for the future time, and generatingcooking instructions to achieve the target cooking state. In a specificexample, the system can automatically instruct the oven to applyindirect heat (e.g., by lowering the radiation output of a heatingelement proximal the foodstuff and increasing the radiation output of aheating element distal the foodstuff) in response to determination thatthe foodstuff exterior is cooking faster than a target rate and/or thefoodstuff interior is cooking slower than a second target rate.

In a second example, the method includes determining the current ovenoperation parameter, comparing the current operation parameter to thetarget operation parameter for the current time, and generatinginstructions to adjust the current operation parameter to substantiallymatch the target operation parameter. In a specific example, the methodcan include measuring the current oven cavity temperature, determiningthat the oven cavity temperature is lower than the target temperature(e.g., determined from a recipe or from a target foodstuff parameter),and increasing the radiation output of the heating elements tosubstantially meet the target temperature.

In a third example, the method includes determining the current ovenoperation parameter, estimating a future oven parameter value, andgenerating instructions to adjust the current operation parameter tosubstantially match the target operation parameter for the future time.In a specific example, the system can determine the current cavitytemperature, estimate a future cavity temperature given the currentairflow and/or radiation output of the heating elements 300, generateinstructions to increase airflow and/or decrease heating element outputif the future cavity temperature exceeds the target future cavitytemperature, and generate instructions to decrease airflow and/orincrease heating element output if the future cavity temperature fallsbelow the target future cavity temperature. However, oven operationinstructions can be otherwise generated for the instantaneous cookingsession.

In a fourth variation, the method includes receiving cookinginstructions from a user device, which allows the user to remotelycontrol oven operation. The cooking instructions can be received beforefoodstuff insertion into the cooking cavity, after foodstuffidentification, before control instruction retrieval, after controlinstruction retrieval, during the cooking session (e.g., wherein theoven dynamically responds to the operation instructions), or at anyother suitable time. The cooking instructions can be used to modify apredetermined set of cooking instructions, be used in lieu of thepredetermined set of cooking instructions, or be otherwise used. In oneexample, the method can include receiving foodstuff parameter valuesfrom the user device (e.g., “crispier”), determining oven operationinstructions to achieve the foodstuff parameter values based on theinstantaneous oven operation parameters (e.g., by the oven or remotecomputing system), and control the oven to operate according to thedetermined operation instructions. In a second example, the method caninclude receiving cooking element operation instructions from the userdevice (e.g., increase right fan speed, rotate both fans in the samedirection, rotate both fans in opposing directions, increase the heatoutput by the back heating element, etc.) and automatically operate thecooking element according to the cooking element operation instructions.However, the cooking instructions can be otherwise utilized.

The user-specified oven cooking instructions can additionally be used toadjust subsequent cooking instructions for the same or related foodclasses for the user, oven, general population of ovens, and/or generalpopulation of users. The cooking instructions can be adjusted by theoven, the remote computing system, the user device, or any othersuitable computing system. The user-specified oven cooking instructionscan additionally be used as a training set to determine the ideal set ofcooking instructions for a food class. The cooking instructions can bereceived directly at the oven (e.g., over a short-range communicationchannel), indirectly through the remote computing system (e.g., througha long-range communication channel), or otherwise received from the userdevice.

In one example, the method can include: receiving cooking elementcontrol instructions from a user device; controlling the cookingelements based on the received cooking element control instructions;sending the received cooking element control instructions to the remotecomputing system, wherein the remote computing system stores the cookingelement control instructions in association with a user accountassociated with the user device; determining a second recipe (e.g.,updated recipe) based on the first recipe and the received cookingelement control instructions; storing the second recipe in the computermemory or by the remote computing system; and in response to subsequentuser selection of the identifier for the foodstuff class, controllingthe cooking elements based on the second recipe. In a second example,multiple cooking instructions can be received from multiple userscooking similar foodstuff in their respective ovens, wherein the remotecomputing system can generate a new set of common cooking instructionsfor the foodstuff class based on the multiple users' cookinginstructions. The new set of common cooking instructions can besubsequently pushed to all ovens, the respective ovens, or any othersuitable oven, wherein the receiving ovens subsequently use the new setof common cooking instructions to cook the foodstuff class. The new setof common cooking instructions can be an average of the received cookinginstructions, a mean of the received cooking instructions, the mostpopular cooking instructions (e.g., cooking instructions shared betweenthe received instructions), or be otherwise determined. However, themethod can otherwise utilize the cooking instructions received from theuser device.

2.4 Dynamic Oven Control Based on the Cooking Instructions.

Dynamically controlling the oven based on the cooking instructions S400functions to operate the oven based on the cooking instructions. Theoven is preferably automatically controlled to execute the cookinginstructions, but can alternatively be controlled according to defaultcooking instructions, manually controlled, or otherwise controlled. Theprocessor preferably automatically controls cooking element operation,but any other suitable computing system can control cooking elementoperation. Cooking elements can include: heating elements, convectionelements, fluid injection elements (e.g., steam, smoke, etc.), vents orvalves, or any other suitable oven element.

In one variation, the oven receives oven operation instructions from theremote system, mobile device, or other computing system, and controlsthe oven components according to the operation instructions.

In a second variation, the foodstuff within the oven is categorized(e.g., by the oven, by the remote system, by the mobile device, etc.)based on one or more images, the oven receives cooking instructionsassociated with the foodstuff category (e.g., retrieves the cookinginstructions, etc.), and the oven operates (e.g., cooks the food) basedon the received cooking instructions.

In a third variation, a food parameter value is determined (e.g.,internal temperature, etc.; determined by the oven, by the remotesystem, by the mobile device, etc.) at a first time (e.g., a firstcooking stage) and compared to the estimated or target food parametervalue for the foodstuff's cooking stage, wherein the oven can beoperated to rectify any differences in between the determined and targetfood parameter values. In a specific example, in response todetermination that the food is cooking too slowly or is drying out, thesystem can automatically determine steam injection parameters (e.g.,volume, rate, etc.) and control the oven to inject steam into thecooking chamber according to the determined steam injection parameters.The cooking stage can be determined based on the amount of time the foodhas been cooking, the oven temperature pattern over the cooking time,the determined food parameter value (e.g., food reflection, food audio,food color, food emissivity, etc.), or determined in any other suitablemanner.

In a fourth variation, dynamically controlling the cooking elementsincludes: determining operation of an oven component, determining achange in a cooking parameter due to oven component operation, anddynamically adjusting the cooking instructions to accommodate for thechange in the cooking parameter. The cooking parameter and/or cookinginstructions change can be determined by the oven, remote computingsystem, user device, or other computing system. In an example of thefourth variation, dynamically controlling the cooking elements includes:detecting oven door actuation, determining an amount of heat lost to theopen door, and adjusting the cooking instructions to accommodate for theheat loss. Oven door actuation (e.g., door opening) can be detectedusing: a door sensor, light sensor, door accelerometer, the oven camera,or other detection means. The amount of heat lost to the open door canbe determined by monitoring the cooking cavity temperature (e.g., usingoptical methods, cavity temperature sensor, etc.), monitoring thefoodstuff surface temperature, estimating the thermal loss based on ovencomponent operation (e.g., based on the convection element speed,heating element operation, etc.), estimating the thermal loss based onambient environment parameters (e.g., using exterior temperaturesensors, etc.), a combination of the above, or otherwise determining theamount of heat lost to ambient. Adjusting the cooking instructions toaccommodate for heat loss can include adding time to the end of thecooking session, increasing the target temperature, injecting acontrolled volume of steam at a controlled rate, or otherwise adjustingthe cooking instructions. In a specific example, shown in FIG. 27, theamount of heat loss over time can be calculated (e.g., integrated),additional time for the foodstuff is calculated (e.g., based on theamount of heat that needs to be replenished and the temperature), andthe additional time added to the end of the cooking session (e.g., toextend the cooking session). However, cooking parameter changes can beotherwise accommodated.

However, the oven can be otherwise operated based on the cookinginstructions.

2.5 User Notifications.

The method can additionally function to automatically generate usernotifications based on the cooking parameters. The notification can be acooking session completion notification (e.g., “ready to eat!”), anerror notification, an instruction notification, or be any othersuitable notification. The notification can be sent and/or displayed onthe user device, oven display, or on any other suitable computingsystem. In one variation, the cooking session completion notification isgenerated in response to a target cooking parameter value being met. Inone example, the notification is generated when a target cooking time ismet. In a second example, the notification is generated when a targetfood parameter value, such as a target internal temperature, surfacebrowning, or internal water content, is met. In a third example, thenotification is generated when an instantaneous time falls within apredetermined time duration from the time to cooking completion, whereinthe time to cooking completion is estimated based on the cooking statusof the in-situ foodstuff. However, any other suitable notification canbe generated in response to the occurrence of any other suitable event.

2.6 Streaming.

The method can additionally include sending the oven measurements fromthe oven to the remote computing system. This can function to providethe remote computing system with a rich source of cooking data fromwhich to develop cooking instructions, updated identification modules,or other data structures. The remote computing system preferably storesall the oven measurements received from all ovens, but can alternativelyextract and store features from oven measurements and discard the ovenmeasurements or otherwise process the oven measurements. The remotecomputing system can additionally use the oven measurements from one ormore ovens over one or more cooking sessions to automatically:accommodate for sensor degradation or calibration slip), generate anaverage cooking curve for a foodstuff class, create oven measurementvisualizations (e.g., charts, .gifs, etc.), generate foodstuffpredictions (e.g., finish time, etc.), perform population-level analyses(e.g., most common food for a given location or demographic), orotherwise use the oven measurements. The remote computing system canoptionally stream all or a portion of the received oven measurements toa user device, user-specified endpoint (e.g., URI, received from theuser device or otherwise associated with the user account), or otherendpoint (e.g., in response to application activity, etc.), wherein theremote computing system can identify the second endpoint in response tooven measurement and/or other trigger condition receipt. Alternativelyor additionally, the oven measurements can be sent directly to the userdevice from the oven. The oven measurements are preferably timestampedand associated with an oven identifier, but can be associated with anyother suitable information. The oven measurements are preferablyrecorded and sent during the foodstuff cooking session, but canalternatively be sent afterward. The oven measurements can be sent inreal- or near-real time (e.g., streamed), sent periodically, sent inresponse to receipt of a request, or sent at any other suitable time.All or a subset of the oven measurements (e.g., every other frame, etc.)can be sent to the remote computing system and/or user device.

2.7 Specific Examples.

In one example, the oven or remote system can identify the food beingcooked and/or dynamically control oven operation to achieve a desiredcooking parameter (e.g., based on a recipe, user preferences, etc.). Themethod can include recording a video or set of images of the foodstuffwithin the oven cavity while cooking, analyzing the images and/or audioof the cooking foodstuff, and categorizing the foodstuff. The foodstuffcategorization can be sent to and/or displayed on the oven or anassociated device (e.g., a user's smartphone), be used to retrievecooking instructions (e.g., preparation preferences for the user, suchas crispness preferences), or be used in any other suitable manner. Theimages can additionally be analyzed to extract the foodstuff cookingprogress, wherein cooking instructions can be retrieved or generated toachieve a target foodstuff preparation state. In a specific example, thesystem can determine that the chicken is below a threshold level ofcrispness (e.g., based on image analysis or audio analysis) and generateheating element instructions (e.g., newly generated or by modifying apre-existing set of instructions or recipe) to achieve the desiredchicken skin crispness. The oven can subsequently receive the heatingelement instructions and control the heating element according to theinstructions.

In a second example, the oven can automatically cook food based on apredetermined recipe. The recipe can be provided by the remote system, asecondary user, the primary user, or by any other suitable entity. Therecipe can additionally be automatically or manually adjusted to meetthe primary user's preferences. In a specific example, the recipe caninclude a set of oven operating instructions (e.g., temperatures, powerprovision to the heating elements 300, which heating elements tocontrol, airflow, etc.) and/or food parameter targets (e.g.,temperatures, colors, sounds, etc). For example, a chicken recipe caninclude a heating element temperature profile including a first timeperiod of elevated temperature for all heating elements 300, a secondtime period of low temperature for the heating element distal thechicken, and a third time period of broiling temperature for the heatingelement proximal the chicken (e.g., proximal the skin-side of thechicken).

In a third example, the oven can automatically record and/or augmentcooking instructions for a recipe generated by the primary user. In thisexample, the user can create or author a recipe concurrently with recipepreparation (e.g., concurrently with cooking the food). A computingsystem can automatically populate the cooking instructions portion ofthe recipe with the cooking parameters of the food, as it is prepared bythe user. In this example, the user can select to author a recipe on auser device and enter the ingredients and/or general cooking steps. Theremote system can create a new recipe file in response to the authoringselection, and store the ingredients and/or general cooking steps. Inresponse to confirmation that the user is preparing food according tothe recipe, the remote system can automatically store or otherwiseassociate cooking parameters received from the oven (that is associatedwith the user) with the recipe file. The stored cooking parametersrecorded from the primary user's oven can subsequently be used togenerate and/or control a secondary user's oven when the secondary userindicates that they are preparing the same or a related recipe. When thesecondary user prepares a related recipe (e.g., where there are cookingparameter differences), the differences and/or similarities between theprimary user's recipe and the secondary user's recipe can be stored inassociation with the file.

In a fourth example, the oven can automatically interact with or operatebased on auxiliary system operation parameters.

In a first specific example, the oven can be connected to a connectedsous vide system (e.g., wired or wirelessly), wherein the oven canidentify food that had previously been in the sous vide system (e.g.,based on mass, weight, temperature, etc.) and determine cookingparameters for the foodstuff based on the previous sous vide cookingparameters. The oven can additionally determine the time to sous videcooking completion, and automatically pre-heat such that the oven willbe preheated by the time the food is ready for the oven.

In a second specific example, the oven can automatically turn off inresponse to auxiliary connected device determination that the user hasleft the home. For example, a connected door lock or thermostat candetermine that the user is no longer proximal the geofence marked as“home.” The remote computing system, oven, or auxiliary connected devicecan determine that the oven is on (e.g., cooking), and generate and senda request to turn off the oven to the user device. The oven canautomatically turn off in response to receipt of a “turn off” responsefrom the user device. Alternatively, the oven can automatically turn offin response to completion of the cooking session (e.g., according to therecipe), in response to determination that the user is no longerproximal the geofence, or operate in any other suitable manner at anyother suitable time.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the various system components andthe various method processes.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for controlling a connected oven, comprising: inresponse to detecting a food insertion event: at a camera opticallyconnected to the cooking cavity of the connected oven, sampling a firstimage of the cooking cavity, the camera arranged along the top of thecooking cavity and directed toward a bottom of the cooking cavity;automatically identifying a food class based on the first image; andautomatically determining an initial browning color for the food basedon the first image; automatically determining a set of cookinginstructions, comprising a termination condition, based on the foodclass and a set of user preferences, wherein the termination conditionis not based on a clock time and comprises an absolute change inbrowning color; automatically initiating a cooking session bycontrolling the cooking apparatus based on the set of cookinginstructions; iteratively, during the cooking session: samplingsuccessive images of the food with the optical sensor; and determining asuccessive browning color for the food based on the successive images;automatically ceasing the cooking session in response to a differencebetween the initial browning color and the successive browning colormeeting or exceeding the absolute color change; and notifying the userof cooking completion.
 2. A method for controlling a cooking apparatuscomprising: at an optical sensor optically connected to a cooking cavityof the cooking apparatus, sampling a first image of the cooking cavity;determining a food class for the food based on the first image;automatically determining a set of cooking instructions based on thefood class and a set of user preferences, wherein the set of cookinginstructions comprises a visual termination condition, determined basedon the set of stored user preferences, that is not based on a clocktime; automatically initiating a cooking session by controlling thecooking apparatus based on the set of cooking instructions; during thecooking session, iteratively: sampling successive images of the foodwith the optical sensor; and determining a food appearance for the foodbased on the successive images of the food; and in response to the foodappearance satisfying the visual termination condition, automaticallyterminating the cooking session.
 3. The method of claim 2, furthercomprising: determining an initial food appearance based on the firstimage; estimating a time to cooking completion based on a clock time anda comparison between the food appearance, determined based on asuccessive image, and the initial food appearance; and displaying thetime to cooking completion on a user interface connected to the cookingapparatus.
 4. The method of claim 3, further comprising: determining afaster-than-expected cooking rate of a food exterior based on thecomparison, and in response to determining the faster-than-expectedcooking rate, dynamically modifying the cooking instructions.
 5. Themethod of claim 4, wherein dynamically modifying the cookinginstructions comprises: decreasing a radiation output of a first heatingelement of the set of heating elements proximal the food; and increasinga radiation output of a second heating element of the set of heatingelements distal the food.
 6. The method of claim 2, wherein the set ofuser preferences comprise a target appearance of the food; wherein thevisual termination condition comprises an absolute color change,associated with the target appearance, for the food; wherein the foodappearance comprises a successive food color for the food; the methodfurther comprising: determining an initial food color based on the firstimage; and for each successive food color, comparing the absolute colorchange to a difference between the initial food color and the successivefood color; wherein the food appearance satisfies the visual terminationcondition when the difference meets or exceeds the absolute colorchange.
 7. The method of claim 6, wherein the target appearancecomprises a browning color.
 8. A method for controlling a cookingapparatus, comprising: sampling a first image of food within the cookingapparatus; determining a food class for the food based on the firstimage; automatically determining a set of cooking instructions based onthe food class and a set of user preferences, the set of cookinginstructions comprising an end condition that is not based on a clocktime; automatically initiating a cooking session by controlling thecooking apparatus based on the set of cooking instructions; whilecontrolling the cooking apparatus, iteratively determining a food statefor the food based on successive images of the food; and automaticallyceasing the cooking session in response to the food state satisfying theend condition.
 9. The method of claim 8, further comprising determiningan initial food color based on the first image, wherein the endcondition comprises a predetermined amount of color change from theinitial food color.
 10. The method of claim 8, further comprising:automatically determining a size of the food based on the first image;wherein the set of cooking instructions is determined based on the sizeof the food.
 11. The method of claim 8, further comprising determining afood distribution within the cooking apparatus, wherein the set ofcooking instructions are determined based on the food distribution. 12.The method of claim 11, wherein determining the set of cookinginstructions based on the food distribution comprises operating heatingelements proximal the food and not operating heating elements distal thefood.
 13. The method of claim 8, further comprising determining a numberof instances of the food within the cooking apparatus, wherein the setof cooking instructions are determined based on the number of instances.14. The method of claim 8, further comprising automatically detecting afood insertion event, wherein the first image is sampled in response todetecting the food insertion event.
 15. The method of claim 8, whereinthe set of user preferences comprises a set of historical userpreferences.
 16. The method of claim 15, wherein the first image issampled at a first time, the method further comprising automaticallydetermining the set of user preferences, comprising: aggregatinghistoric settings for the food class for the cooking apparatus, whereineach historic setting is associated with a timestamp; determining a setof historic settings having timestamps associated with the first time;and determining the set of user preferences based on the historicsettings.
 17. The method of claim 8, wherein the set of user preferencesis received from a user.
 18. The method of claim 8, further comprising:based on the food state, dynamically modifying a thermal distribution ofthe cooking cavity, comprising: decreasing a radiation output of a firstheating element of the set of heating elements; and increasing aradiation output of a second heating element of the set of heatingelements.
 19. The method of claim 8, wherein the end condition comprisesa browning color for the food.
 20. The method of claim 8, wherein thefirst image and successive images are sampled by an optical sensormounted within the cooking apparatus with a field of view directedtoward the food.